Mixing patterns in networks

ERCnet: Phylogenomic Prediction of Interaction Networks in the Presence of Gene Duplication

Assigning gene function from genome sequences is a rate-limiting step in molecular biology research. A protein's position within an interaction network can potentially provide insights into its molecular mechanisms. Phylogenetic analysis of evolutionary rate covariation (ERC) in protein sequence has been shown to be effective for large-scale prediction of functional relationships and interactions. However, gene duplication, gene loss, and other sources of phylogenetic incongruence are barriers for analyzing ERC on a genome-wide basis. Here, we developed ERCnet, a bioinformatic program designed to overcome these challenges, facilitating efficient all-versus-all ERC analyses for large protein sequence datasets. We simulated proteome datasets and found that ERCnet achieves combined false positive and negative error rates well below 10% and that our novel "branch-by-branch" length measurements outperforms "root-to-tip" approaches in most cases, offering a valuable new strategy for performing ERC. We also compiled a sample set of 35 angiosperm genomes to test the performance of ERCnet on empirical data, including its sensitivity to user-defined analysis parameters such as input dataset size and branch-length measurement strategy. We investigated the overlap between ERCnet runs with different species samples to understand how species number and composition affect predicted interactions and to identify the protein sets that consistently exhibit ERC across angiosperms. Our systematic exploration of the performance of ERCnet provides a roadmap for design of future ERC analyses to predict functional interactions in a wide array of genomic datasets. ERCnet code is freely available at https://github.com/EvanForsythe/ERCnet.

Homophily and gender differences in unhealthy weight control behaviors among adolescents: a longitudinal school-based friendship network study

BACKGROUND

Unhealthy weight control behaviors (UWCBs) are widespread in adolescents and have devastating consequences. Friendship network is potentially an important influence on university students' UWCBs. This study employs social network analysis to investigate the homophily of UWCBs among university students within peer networks and gender differences.

METHODS

A longitudinal study comprising two data collection waves was conducted on 612 undergraduate students from five schools at two universities in Guangdong Province, China, forming five sociometric networks. UWCBs, incorrect self-perception of obesity, depression, and anxiety were measured using standard scales. Descriptive statistics, assortativity tests and network autocorrelation models were utilized for data analysis.

RESULTS

Study recruited 214 (34.8%) males and 398 (65.2%) females, with the UWCBs prevalence of 28.6% (T0) and 30.0% (T1). A significant homophily of UWCBs enhancing with time was identified (ρT0 = 0.255, p = 0.046; ρT1 = 0.394, p = 0.020). Females (β = 0.185, p = 0.027), overweight (β = 0.673, p < 0.001), obesity (β = 0.499, p < 0.001), incorrect self-perception of obesity (β = 0.538, p < 0.001), depression (β = 0.264, p = 0.025) and lower network transitivity (β = -0.375, p = 0.048) were associated with higher level of UWCBs. Additionally, significant homophily in UWCBs was found among females (ρT0 = 0.340, p = 0.035; ρT1 = 0.412, p = 0.026) but not males (ρT0 = -0.031, p = 0.178; ρT1 = -0.065, p = 0.551). Significant gender differences were also observed in others risk factors of UWCBs.

CONCLUSIONS

Our study found significant homophily and gender differences in UWCBs among first-year university students. These findings highlight the importance of considering peer networks and gender in future interventions.

An Event-Link Network Model Based on Representation in P-Space

The L-space and P-space are two essential representations for studying complex networks that contain different clusters. Existing network models can successfully generate networks in L-space, but generating networks in P-space poses significant challenges. In this study, we present an empirical analysis of the distribution of the number of a line's nodes and the properties of the networks generated by these data in P-space. To gain insights into the operational mechanisms of the network of these data, we propose an event-link model that incorporates new nodes and links in P-space based on actual data characteristics using real data from marine and public transportation networks. The entire network consists of a series of events that consist of many nodes, and all nodes in an event are connected in the P-space. We conduct simulation experiments to explore the model's topological features under different parameter conditions, demonstrating that the simulation outcomes are consistent with the theoretical analysis of the model. This model exhibits small-world characteristics, scale-free behavior, and a high clustering coefficient. The event-link model, with its adjustable parameters, effectively generates networks with stable structures that closely resemble the statistical characteristics of real-world networks that share similar growth mechanisms. Moreover, the network's growth and evolution can be flexibly adjusted by modifying the model parameters.

Estimating Brain Similarity Networks with Diffusion MRI

Structural similarity has emerged as a promising tool in mapping the network organization of an individual, living human brain. Here, we propose diffusion similarity networks (DSNs), which employ rotationally invariant spherical harmonic features derived from diffusion magnetic resonance imaging (dMRI), to map gray matter structural organization. Compared to prior approaches, DSNs showed clearer laminar, cytoarchitectural, and micro-architectural organization; greater sensitivity to age, cognition, and sex; higher heritability in a large dataset of healthy young adults; and straightforward extension to non-cortical regions. We show DSNs are correlated with functional, structural, and gene expression connectomes and their gradients align with the sensory-fugal and sensorimotor-association axes of the cerebral cortex, including neuronal oscillatory dynamics, metabolism, immunity, and dopaminergic and glutaminergic receptor densities. DSNs can be easily integrated into conventional dMRI analysis, adding information complementary to structural white matter connectivity, and could prove useful in investigating a wide array of neurological and psychiatric conditions.

Leveraging advances in machine learning for the robust classification and interpretation of networks

The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often, simulation approaches involve selecting a suitable network generative model such as Erdös-Rényi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes, using both primary features and their interactions. Our study underscores the significance of specific network features and their interactions in distinguishing generative models, comprehending complex network structures and the formation of real-world networks.

Worldwide connections of influencers who promote e-cigarettes on Instagram and TikTok: a social network analysis

Exposure to e-cigarette marketing on social media is a risk factor for e-cigarette use among youth. Tobacco brands use influencers to promote e-cigarettes on social media; however, influencer marketing has not been sufficiently studied. This study explored network connections and interactions through comments on social media posts between global nano- and micro-influencers (influencers with approximately 1,000 to 100,000 followers) and their audiences on Instagram and TikTok. We constructed directed unipartite networks among Instagram (N = 104) and TikTok (N = 100) influencers and users on Instagram (N = 55,622) and TikTok (N = 68,673) who commented on these influencers' posts in 2021-2022 (including influencers who commented on each other's posts). Comments to posts of users who were not classified as influencers were not collected. The Instagram network was denser (more interconnected) and active compared to the TikTok network (1.48 times higher density, 281 times higher transitivity, and 85 times higher reciprocity). Both Instagram and TikTok networks contained heterophilic ties (i.e., influencers from different geographic regions such as Asia, North America and Europe connected to each other), indicating that influencers from different geographic regions engage with (comment on) each other's content, potentially exposing audiences to a wide variety of e-cigarette content. Influencers who promote e-cigarettes and post about lifestyle topics (e.g., fitness, fashion, gaming) occupy more central positions in the Instagram and TikTok networks than influencers who focus primarily on e-cigarette promotion, potentially exposing users who are not interested in tobacco-related content to harmful imagery of e-cigarettes. The findings emphasize the need for strengthening influencer marketing regulation on social media platforms popular among youth.

A Spatiotemporal Analysis of a High-Resolution Molecular Network Reveals Shifts of HIV-1 Transmission Hotspots in Guangzhou, China

BACKGROUND

High-resolution and longitudinal HIV molecular surveillance can inform the evolving hotspots to tailor regionally focused control strategies.

METHODS

HIV-1 pol sequences of three predominant genotypes (CRF01_AE, CRF07_BC, and CRF55_01B) were collected for molecular network reconstruction from people living with HIV (PLWH) in Guangzhou (2018-2020). They were categorized by geographical residences into central, suburban, and outer suburban areas. Clustering rates, assortativity coefficients, and intensity matrices were employed to assess transmission dynamics, geographic mixing patterns, and intra- and inter-area transmission, respectively.

RESULTS

Of the 2469 PLWH, 55.5% resided in the central area. Clustering rates showed no significant differences across areas (44.5%, 40.6% vs. 45.7%; p = 0.184). However, the transmission hotspots for CRF01_AE and CRF55_01B shifted to the outer suburban area. PLWH tended to form links within their local area (assortativity coefficient = 0.227, p < 0.001), particularly for CRF01_AE (0.512, p < 0.001; intra-area intensity = 69.2%). The central area exhibited the highest but decreasing intra-area transmission (74.5% to 30.2%), while intra- and inter-area transmission involving the outer suburban area increased (23.1% to 38.2%).

CONCLUSIONS

Despite most PLWH residing in the central area, the outer suburban area emerged as the hotspot, requiring interventions towards both intra- and inter-area transmission.

Cross-validation for training and testing co-occurrence network inference algorithms

BACKGROUND

Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation.

RESULTS

Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability.

CONCLUSIONS

Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method's applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health.

The limits of digital liberation: The social locations of gang-affiliated girls and women in the digital streets

This study investigates the social network structure of an online gang forum, focusing on the social location of gang-affiliated girls and women in the "digital streets." Existing studies highlight how gang members use social media for masculine posturing and promoting violent identities, but there is a significant gap in understanding the digital engagement of girls and women in gangs. Specifically, few studies have directly examined the network positionality of girls and women through social network analysis of digital data. Our research addresses this gap by analyzing user-to-user interactions on a public Facebook page popular among Chicago-area gang members, circa 2015-2016 (4231 positive and negative interactions across 37,403 comments from 6829 user profiles). Digital platforms could offer a space where girls and women who claim gang affiliation can be liberated from analog constraints in establishing gang centrality. Findings indicate, however, that girls and women remain in peripheral network positions, undermining the liberation hypothesis. Our findings challenge optimistic narratives about the liberating potential of social media, underscore the persistence of misogyny in gang culture, and contribute to understanding how digitalization affects gang dynamics.

Weighted-ensemble network simulations of the susceptible-infected-susceptible model of epidemics

The presence of erratic or unstable paths in standard kinetic Monte Carlo simulations significantly undermines the accurate simulation and sampling of transition pathways. While typically reliable methods, such as the Gillespie algorithm, are employed to simulate such paths, they encounter challenges in efficiently identifying rare events due to their sequential nature and reliance on exact Monte Carlo sampling. In contrast, the weighted-ensemble method effectively samples rare events and accelerates the exploration of complex reaction pathways by distributing computational resources among multiple replicas, where each replica is assigned a weight reflecting its importance, and evolves independently from the others. Here, we implement the highly efficient and robust weighted-ensemble method to model susceptible-infected-susceptible dynamics on large heterogeneous population networks, and explore the interplay between stochasticity and contact heterogeneity, which ultimately gives rise to disease clearance. Studying a wide variety of networks characterized by fat-tailed asymmetric degree distributions, we are able to compute the mean time to extinction and quasistationary distribution around it in previously inaccessible parameter regimes.

Artificial intelligence chatbots mimic human collective behaviour

Artificial Intelligence (AI) chatbots, such as ChatGPT, have been shown to mimic individual human behaviour in a wide range of psychological and economic tasks. Do groups of AI chatbots also mimic collective behaviour? If so, artificial societies of AI chatbots may aid social scientific research by simulating human collectives. To investigate this theoretical possibility, we focus on whether AI chatbots natively mimic one commonly observed collective behaviour: homophily, people's tendency to form communities with similar others. In a large simulated online society of AI chatbots powered by large language models (N = 33,299), we find that communities form over time around bots using a common language. In addition, among chatbots that predominantly use English (N = 17,746), communities emerge around bots that post similar content. These initial empirical findings suggest that AI chatbots mimic homophily, a key aspect of human collective behaviour. Thus, in addition to simulating individual human behaviour, AI-powered artificial societies may advance social science research by allowing researchers to simulate nuanced aspects of collective behaviour.

HIV risk and influence factors among MSM who had sought sexual partners in core venues: a continuous sentinel surveillance in 2010-2022

Background

Seeking sexual partners in men who have sex with men (MSM) venues has been regarded as a high-risk behavior for HIV among MSM. Nevertheless, with the implementation of venue-based interventions and the change in the way MSM seek sexual partners, the continued status of MSM venues as the HIV risk factor remains inconclusive. This study endeavors to delve into this ambiguity by examining the MSM sexual contact network (SCN) as a foundation.

Methods

A series of cross-sectional surveys were conducted in Shenzhen in the period 2010-2022. MSM sexual contact network and venue network were acquired, and network metrics were employed to identify core MSM and core venues. We compared the risk of HIV and risk behaviors between MSM who sought sexual partners in core venues and those who did not, with subgroup analyses based on different time periods.

Results

The overall HIV prevalence among the 4,408 MSM surveyed in this study was 14.6%. Notably, 17 core venues were identified out of the 68 reported MSM venues, with 1,486 MSM who had sought sexual partners in core venues. These MSM had significantly higher risk of HIV and were more likely to take HIV testing and receive intervention services. Subgroup analyses showed that the heightened HIV risk associated with seeking partners in core venues was specific to the period 2010-2014, while HIV testing and service access remained consistently higher across all-period subgroups. Multiple sexual partners, seeking partners in core venues, receptive or both sexual roles, drug abuse, absence of HIV test, unprotected anal intercourse (UAI), and lower education levels were associated with elevated HIV risk among MSM.

Conclusion

Following the implementation of differentiated venue-based interventions, the risk of HIV among MSM who had sought sexual partners in core venues decreased to a level comparable to that of MSM who had not. The accessibility of HIV testing and intervention services remains uneven between MSM who had sought sexual partners in core venues and those who had not. As the Internet sex-seeking behavior gains prevalence among MSM, strategic adjustments of public health resource allocation may be necessary to address this imbalance.

Increase of niche filling with increase of host richness for plant-infecting mastreviruses

Now that it has been realized that viruses are ubiquitous, questions have been raised on factors influencing their diversity and distribution. For phytoviruses, understanding the interplay between plant diversity and virus species richness and prevalence remains cardinal. As both the amplification and the dilution of viral species richness due to increasing host diversity have been theorized and observed, a deeper understanding of how plants and viruses interact in natural environments is needed to explore how host availability conditions viral diversity and distributions. From a unique dataset, this study explores interactions of Mastrevirus species (family Geminiviridae) with Poales order hosts across 10 sites from three contrasting ecosystems on La Réunion. Among 273 plant pools, representing 61 Poales species, 15 Mastrevirus species were characterized from 22 hosts. The analysis revealed a strong association of mastreviruses with hosts from agroecosystems, the rare presence of viruses in coastal grasslands, and the absence of mastreviruses in subalpine areas, areas dominated by native plants. This suggests that detected mastreviruses were introduced through anthropogenic activities, emphasizing the role of humans in shaping the global pathobiome. By reconstructing the realized host-virus infection network, besides revealing a pattern of increasing viral richness with increasing host richness, we observed increasing viral niche occupancies with increasing host species richness, implying that virus realized richness at any given site is conditioned on the global capacity of the plant populations to host diverse mastreviruses. Whether this tendency is driven by synergy between viruses or by an interplay between vector population and plant richness remains to be established.

Promotion of cooperation in a structured population with environmental feedbacks

Cooperation is a representative altruistic behavior in which individuals contribute public goods to benefit their neighborhoods and even larger communities in social networks. The defective behavior is more likely to bring higher payoffs than the cooperative behavior, which makes the cooperative behavior hard to maintain and sustain. Many mechanisms were proposed to promote cooperation within a social dilemma, in which some recent studies introduced the impact of dynamically changing environments on players' payoffs and strategies in social-ecological systems, and evolutionary-ecological systems. However, degree heterogeneity, an important structural property of many real-world complex networks such as social networks, academic collaboration networks, and communication networks, is rarely explored and studied in such eco-evolutionary games. In this research, we propose a Public Goods Game model on social networks with environmental feedback and analyze how the environmental factor and network structure affect the evolution of cooperation. It is found that as the initial environmental factors and the cooperation-enhancement defection-degradation ratio increase, the steady cooperation level of the social network significantly increases, and the dynamic environment will eventually evolve into a high-return environment; On the other hand, even if the initial environmental benefit coefficient is high, when the cooperation-enhancement defection-degradation ratio is less than a threshold, the dynamic environment will gradually degrade into a low-return environment. The steady cooperation level of the social network first gradually increases as the network structure becomes more heterogeneous, but it will decrease once the heterogeneity of the social network exceeds a certain threshold.

Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in functional brain connectivity and structural alterations in large-scale brain networks. Although sensory pathway anomalies have been implicated in ADHD, the exploration of sensory integration regions remains limited. In this study, we adopted an exploratory approach to investigate the connectivity profile of auditory-visual integration networks (AVIN) in children with ADHD and neurotypical controls using the ADHD-200 rs-fMRI dataset. We expanded our exploration beyond network-based statistics (NBS) by extracting a diverse range of graph theoretical features. These features formed the basis for applying machine learning (ML) techniques to discern distinguishing patterns between the control group and children with ADHD. To address class imbalance and sample heterogeneity, we employed ensemble learning models, including balanced random forest (BRF), XGBoost, and EasyEnsemble classifier (EEC). Our findings revealed significant differences in AVIN between ADHD individuals and neurotypical controls, enabling automated diagnosis with moderate accuracy (74.30%). Notably, the EEC model demonstrated balanced sensitivity and specificity metrics, crucial for diagnostic applications, offering valuable insights for potential clinical use. These results contribute to understanding ADHD's neural underpinnings and highlight the diagnostic potential of AVIN measures. However, the exploratory nature of this study underscores the need for future research to confirm and refine these findings with specific hypotheses and rigorous statistical controls.

The association between structural connectivity and anti-seizure medication response in patients with temporal lobe epilepsy

OBJECTIVES

This study aimed to investigate the differences in structural connectivity and glymphatic system function between patients with temporal lobe epilepsy (TLE) and hippocampal sclerosis (HS) and healthy controls. Additionally, we analyzed the association between structural connectivity, glymphatic system function, and antiseizure medication (ASM) response.

METHODS

We retrospectively enrolled patients with TLE and HS and healthy controls who underwent diffusion tensor imaging at our hospital. We assessed structural connectivity in patients with TLE and HS and healthy controls by calculating network measures using graph theory and evaluated glymphatic system function using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Patients with TLE and HS were categorized into two groups: ASM poor and good responders.

RESULTS

We enrolled 55 patients with TLE and HS and 53 healthy controls. Of the 55 patients with TLE and HS, 39 were ASM poor responders, and 16 were ASM good responders. The assortativity coefficient in patients with TLE and HS was higher than that in healthy controls (0.004 vs. -0.007, p = 0.004), and the assortativity coefficient in ASM poor responders was lower than that in ASM good responders (-0.001 vs. -0.197, p = 0.003). The DTI-ALPS index in patients with TLE and HS was lower than that in healthy controls (1.403 vs. 1.709, p < 0.001); however, the DTI-ALPS index did not differ between ASM poor and good responders (1.411 vs. 1.385, p = 0.628). The DTI-ALPS index had a significant negative correlation with age in patients with TLE and HS (r = -0.267, p = 0.049).

SIGNIFICANCE

We confirmed increased assortativity coefficient in structural connectivity and decreased DTI-ALPS index in patients with TLE and HS compared with healthy controls. Additionally, we demonstrated an association between decreased assortativity coefficient in structural connectivity and ASM poor response in patients with TLE patients and HS.

PLAIN LANGUAGE SUMMARY

This study investigates the relationship between brain connectivity changes and glymphatic system function with antiseizure medication response in patients with temporal lobe epilepsy and hippocampal sclerosis. The research reveals that these patients show altered brain connectivity and glymphatic function compared to healthy individuals. A key finding is the strong link between a specific connectivity measure (assortativity coefficient) and antiseizure medication response, providing valuable insights that could influence epilepsy treatment and future research directions.

Polyclonal regeneration of mouse bone marrow endothelial cells after irradiative conditioning

Bone marrow endothelial cells (BM-ECs) are the essential components of the BM niche and support the function of hematopoietic stem cells (HSCs). However, conditioning for HSC transplantation causes damage to the recipients' BM-ECs and may lead to transplantation-related morbidity. Here, we investigated the cellular and clonal mechanisms of BM-EC regeneration after irradiative conditioning. Using single-cell RNA sequencing, imaging, and flow cytometry, we revealed how the heterogeneous pool of BM-ECs changes during regeneration from irradiation stress. Next, we developed a single-cell in vitro clonogenic assay and demonstrated that all EC fractions hold a high potential to reenter the cell cycle and form vessel-like structures. Finally, we used Rainbow mice and a machine-learning-based model to show that the regeneration of BM-ECs after irradiation is mostly polyclonal and driven by the broad fraction of BM-ECs; however, the cell output among clones varies at later stages of regeneration.

SkyMap: a generative graph model for GNN benchmarking

Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 benchmark datasets for validation. To address this limitation, several generative graph models like ALBTER or GenCAT have emerged, aiming to fix this problem with synthetic graph datasets. However, these models often struggle to mirror the GNN performance of the original graphs. In this work, we present SkyMap, a generative model for labeled attributed graphs with a fine-grained control over graph topology and feature distribution parameters. We show that our model is able to consistently replicate the learnability of graphs on graph convolutional, attention, and isomorphism networks better (64% lower Wasserstein distance) than ALBTER and GenCAT. Further, we prove that by randomly sampling the input parameters of SkyMap, graph dataset constellations can be created that cover a large parametric space, hence making a significant stride in crafting synthetic datasets tailored for GNN evaluation and benchmarking, as we illustrate through a performance comparison between a GNN and a multilayer perceptron.

The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma

Ovarian cancer is the deadliest gynecologic malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes. To improve spatial understanding of the TIME, we performed multiplexed ion beam imaging on 83 human high-grade serous carcinoma tumor samples, identifying approximately 160,000 cells across 23 cell types. From the 77 of these samples that met inclusion criteria, we generated composition features based on cell type proportions, spatial features based on the distances between cell types, and spatial network features representing cell interactions and cell clustering patterns, which we linked to traditional clinical and IHC variables and patient overall survival (OS) and progression-free survival (PFS) outcomes. Among these features, we found several significant univariate correlations, including B-cell contact with M1 macrophages (OS HR = 0.696; P = 0.011; PFS HR = 0.734; P = 0.039). We then used high-dimensional random forest models to evaluate out-of-sample predictive performance for OS and PFS outcomes and to derive relative feature importance scores for each feature. The top model for predicting low or high PFS used TIME composition and spatial features and achieved an average AUC score of 0.71. The results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

Maximum entropy in dynamic complex networks

The field of complex networks studies a wide variety of interacting systems by representing them as networks. To understand their properties and mutual relations, the randomization of network connections is a commonly used tool. However, information-theoretic randomization methods with well-established foundations mostly provide a stationary description of these systems, while stochastic randomization methods that account for their dynamic nature lack such general foundations and require extensive repetition of the stochastic process to measure statistical properties. In this work, we extend the applicability of information-theoretic methods beyond stationary network models. By using the information-theoretic principle of maximum caliber we construct dynamic network ensemble distributions based on constraints representing statistical properties with known values throughout the evolution. We focus on the particular cases of dynamics constrained by the average number of connections of the whole network and each node, comparing each evolution to simulations of stochastic randomization that obey the same constraints. We find that ensemble distributions estimated from simulations match those calculated with maximum caliber and that the equilibrium distributions to which they converge agree with known results of maximum entropy given the same constraints. Finally, we discuss further the connections to other maximum entropy approaches to network dynamics and conclude by proposing some possible avenues of future research.

DPGCL: Dual pass filtering based graph contrastive learning

Graph Contrastive Learning (GCL), which learns node or graph representation from supervision signals derived from the graph data itself, has recently attracted extensive research attention and achieved great success. Remarkably, most of the existing GCL encoders essentially perform low-frequency filtering on graph, which however limits their expressive power on heterophilous graphs where dissimilar nodes tend to be connected. This raises an interesting question: can high frequency be informative for GCL? In this work, we experimentally study the influence of high-frequency signals on GCL and find that adding some high-frequency signals in contrasting is beneficial for improving GCL performance. That motivates us to design a more general GCL framework beyond low-pass filtering, which simultaneously performs low-pass and high-pass signal contrasts, so as to capture both low and high-frequency information in general graphs. Furthermore, to enable the representation learning to be aware of neighbor diversity in heterophilic graphs, we propose a novel graph contrastive loss, termed Adap-infoNCE, which can automatically decide the weights of negative samples based on feature representations of neighboring nodes. Here two types of neighbors are considered, i.e., spatial neighbors and featural neighbors, whose effectiveness is verified using empirical study on synthetic datasets. Extensive experiments demonstrate that our method brings significant and consistent improvements over the base GCL approach and exceeds multiple state-of-the-art results on several unsupervised benchmarks, even surpassing the performance of supervised benchmarks.

Deep learning resilience inference for complex networked systems

Resilience, the ability to maintain fundamental functionality amidst failures and errors, is crucial for complex networked systems. Most analytical approaches rely on predefined equations for node activity dynamics and simplifying assumptions on network topology, limiting their applicability to real-world systems. Here, we propose ResInf, a deep learning framework integrating transformers and graph neural networks to infer resilience directly from observational data. ResInf learns representations of node activity dynamics and network topology without simplifying assumptions, enabling accurate resilience inference and low-dimensional visualization. Experimental results show that ResInf significantly outperforms analytical methods, with an F1-score improvement of up to 41.59% over Gao-Barzel-Barabási framework and 14.32% over spectral dimension reduction. It also generalizes to unseen topologies and dynamics and maintains robust performance despite observational disturbances. Our findings suggest that ResInf addresses an important gap in resilience inference for real-world systems, offering a fresh perspective on incorporating data-driven approaches to complex network modeling.

Elevated Atmospheric Co2 Levels Impact Soil Protist Functional Core Community Compositions

Protists, known as microeukaryotes, are a significant portion of soil microbial communities. They are crucial predators of bacteria and depend on bacterial community dynamics for the growth and evolution of protistan communities. In parallel, increased levels of atmospheric CO2 significantly impact bacterial metabolic activity in rhizosphere soils. In this study, we investigated the effect of elevated atmospheric CO2 levels on the metabolically active protist community composition and function and their co-occurrences with bacteria from bulk and rhizosphere soils from the Giessen Free-Air CO2 enrichment grassland experiment. Metabarcoding sequencing data analyses of partial 18S rRNA from total soil RNA showed that elevated CO2 concentrations stimulated only a few ASVs of phagotrophic predators of bacteria and other microeukaryotes, affecting protist community composition (P = 0.006, PERMANOVA). In parallel, phagotrophic and parasitic lineages appeared slightly favoured under ambient CO2 conditions, results that were corroborated by microbial signature analyses. Cross-comparisons of protist-bacteria co-occurrences showed mostly negative relations between prokaryotes and microeukaryotes, indicating that the ongoing increase in atmospheric CO2 will lead to changes in microbial soil communities and their interactions, potentially cascading to higher trophic levels in soil systems.

The research of knowledge diffusion network model for Tourism Destination-Public ecological civilization

Visitor education plays a crucial role in the knowledge diffusion process in outdoor recreation and nature-based tourism. It entails sharing information, experiences, and insights with visitors to enhance their understanding and appreciation of the natural environment. Our methodology for investigating the diffusion of ecological civilization knowledge in tourism destinations involves constructing a knowledge diffusion network model. In this model, scenic spots, tourists, and the public are defined as network nodes, with the communication channels between them representing the edges of the network. By constructing a scale-free complex network, the knowledge diffusion mode of scenic spots can be depicted. The layer of resource supply node consists of different scenic spots, forming the core nodes set for knowledge diffusion in the tourism industry. This research aims to further explore the social and economic value of the tourism areas' ecological civilization knowledge diffusion, as well as analyze the path, quantity, and cost of knowledge diffusion. by analyzing this knowledge diffusion network model, insights into the effectiveness and impact of visitor education in promoting ecological civilization and sustainable practices in tourism destinations can be gained. Overall, this approach provides a theoretical framework for investigating and comprehending the knowledge diffusion process in Tourism Destination-Public ecological civilization, thereby shedding light on the social and economic benefits that can be derived from sustainable tourism practices.

Most genetic roots of fungal and animal aging are hundreds of millions of years old according to phylostratigraphy analyses of aging networks

Few studies have systematically analyzed how old aging is. Gaining a more accurate knowledge about the natural history of aging could however have several payoffs. This knowledge could unveil lineages with dated genetic hardware, possibly maladapted to current environmental challenges, and also uncover "phylogenetic modules of aging," i.e., naturally evolved pathways associated with aging or longevity from a single ancestry, with translational interest for anti-aging therapies. Here, we approximated the natural history of the genetic hardware of aging for five model fungal and animal species. We propose a lower-bound estimate of the phylogenetic age of origination for their protein-encoding gene families and protein-protein interactions. Most aging-associated gene families are hundreds of million years old, older than the other gene families from these genomes. Moreover, we observed a form of punctuated evolution of the aging hardware in all species, as aging-associated families born at specific phylogenetic times accumulate preferentially in genomes. Most protein-protein interactions between aging genes are also old, and old aging-associated proteins showed a reduced potential to contribute to novel interactions associated with aging, suggesting that aging networks are at risk of losing in evolvability over long evolutionary periods. Finally, due to reshuffling events, aging networks presented a very limited phylogenetic structure that challenges the detection of "maladaptive" or "adaptative" phylogenetic modules of aging in present-day genomes.

mosna reveals different types of cellular interactions predictive of response to immunotherapies and survival in cancer

Spatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, performing a rational analysis including multiple algorithms while integrating different conditions such as clinical data is still not trivial. To make such investigations more accessible, we developed mosna, a Python package to analyze spatial omics data with respect to clinical or biological data and to gain insight on cell interaction patterns or tissue architecture of biological relevance. mosna is compatible with all spatial omics methods, it leverages tysserand to build accurate spatial networks, and is compatible with Squidpy. It proposes an analysis pipeline, in which increasingly complex features computed at each step can be explored in integration with clinical data, either with easy-to-use descriptive statistics and data visualization, or by seamlessly training machine learning models and identifying variables with the most predictive power. mosna can take as input any dataset produced by spatial omics methods, including sub-cellular resolved transcriptomics (MERFISH, seqFISH) and proteomics (CODEX, MIBI-TOF, low-plex immuno-fluorescence), as well as spot-based spatial transcriptomics (10x Visium). Integration with experimental metadata or clinical data is adapted to binary conditions, such as biological treatments or response status of patients, and to survival data. We demonstrate the proposed analysis pipeline on two spatially resolved proteomic datasets containing either binary response to immunotherapy or survival data. mosna identifies features describing cellular composition and spatial distribution that can provide biological insight regarding factors that affect response to immunotherapies or survival.

Networking the desert plant microbiome, bacterial and fungal symbionts structure and assortativity in co-occurrence networks

In nature, microbes do not thrive in seclusion but are involved in complex interactions within- and between-microbial kingdoms. Among these, symbiotic associations with mycorrhizal fungi and nitrogen-fixing bacteria are namely known to improve plant health, while providing resources to benefit other microbial members. Yet, it is not clear how these microbial symbionts interact with each other or how they impact the microbiota network architecture. We used an extensive co-occurrence network analysis, including rhizosphere and roots samples from six plant species in a natural desert in AlUla region (Kingdom of Saudi Arabia) and described how these symbionts were structured within the plant microbiota network. We found that the plant species was a significant driver of its microbiota composition and also of the specificity of its interactions in networks at the microbial taxa level. Despite this specificity, a motif was conserved across all networks, i.e., mycorrhizal fungi highly covaried with other mycorrhizal fungi, especially in plant roots-this pattern is known as assortativity. This structural property might reflect their ecological niche preference or their ability to opportunistically colonize roots of plant species considered non symbiotic e.g., H. salicornicum, an Amaranthaceae. Furthermore, these results are consistent with previous findings regarding the architecture of the gut microbiome network, where a high level of assortativity at the level of bacterial and fungal orders was also identified, suggesting the existence of general rules of microbiome assembly. Otherwise, the bacterial symbionts Rhizobiales and Frankiales covaried with other bacterial and fungal members, and were highly structural to the intra- and inter-kingdom networks. Our extensive co-occurrence network analysis of plant microbiota and study of symbiont assortativity, provided further evidence on the importance of bacterial and fungal symbionts in structuring the global plant microbiota network.

Endogenous dynamics of denunciation: Evidence from an inquisitorial trial

We develop an endogenous approach to the practice of denunciation, as an alternative to exogenous historical and sociological accounts. It analyzes denunciation as a response to increasing pressure, which in turn increases pressure on social contacts. The research context is the trial of Waldensians in Giaveno, Italy, in 1335, headed by the inquisitor Alberto de Castellario. A dynamic network actor model attests that coercive pressure not only raises the rate of denunciation but also compels denouncers to implicate individuals who are socially closer to them. We find that coercive pressure starts yielding diminishing returns relatively quickly, with the degree of redundancy of information escalating as a result of preferential attachment, increasingly targeting those already denounced by others, publicly announced suspects, and those having absconded from the trial.

Epidemic spreading on mixing group with face-to-face interaction

The mixing groups gathered in the enclosed space form a complex contact network due to face-to-face interaction, which affects the status and role of different groups in social communication. The intricacies of epidemic spreading in mixing groups are intrinsically complicated. Multiple interactions and transmission add to the difficulties of understanding and forecasting the spread of infectious diseases in mixing groups. Despite the critical relevance of face-to-face interactions in real-world situations, there is a significant lack of comprehensive study addressing the unique issues of mixed groups, particularly those with complex face-to-face interactions. We introduce a novel model employing an agent-based approach to elucidate the nuances of face-to-face interactions within mixing groups. In this paper, we apply a susceptible-infected-susceptible process to mixing groups and integrate a temporal network within a specified time window to distinguish between individual movement patterns and epidemic spreading dynamics. Our findings highlight the significant impact of both the relative size of mixing groups and the groups' mixing patterns on the trajectory of disease spread within the mixing groups. When group sizes differ significantly, high inter-group contact preference limits disease spread. However, if the minority reduces their intra-group preferences while the majority maintains high inter-group contact, disease spread increases. In balanced group sizes, high intra-group contact preferences can limit transmission, but asymmetrically reducing any group's intra-group preference can lead to increased spread.

Co-evolving networks for opinion and social dynamics in agent-based models

The rise of digital social media has strengthened the coevolution of public opinions and social interactions that shape social structures and collective outcomes in increasingly complex ways. The existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, such as echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time.

Exploring Within-Gender Differences in Friendships Using an Online Social Network

People tend to befriend others similar to themselves, generating a pattern called homophily. However, existing studies on friendship patterns often rely on surveys that assess the perspective of relatively few participants on their friendships but do not measure actualized friendship patterns. Here, we used data from a large Slovakian online social network to assess the role of gender, age, and body mass index (BMI) in same-gender online connections among more than 400,000 users. We found that age and BMI homophily occurred in both men's and women's same-gender connections, but somewhat more strongly among men's. Yet, as women diverged in BMI, their connections were less likely to be reciprocated. We discuss how the evolutionary legacy of men's coalitional competition (e.g., warfare) and women's mating competition or recruitment of allocare providers might contribute to these patterns in modern same-gender relationships. For example, men's engagement in physical activities may lead to similar formidability levels among their same-gender peers. Altogether, our findings highlight the importance of trait similarity to same-gender friendship patterns.

A dandelion structure of eigenvector preferential attachment networks

In this paper we introduce a new type of preferential attachment network, the growth of which is based on the eigenvector centrality. In this network, the agents attach most probably to the nodes with larger eigenvector centrality which represents that the agent has stronger connections. A new network is presented, namely a dandelion network, which shares some properties of star-like structure and also a hierarchical network. We show that this network, having hub-and-spoke topology is not generally scale free, and shows essential differences with respect to the Barabási-Albert preferential attachment model. Most importantly, there is a super hub agent in the system (identified by a pronounced peak in the spectrum), and the other agents are classified in terms of the distance to this super-hub. We explore a plenty of statistical centralities like the nodes degree, the betweenness and the eigenvector centrality, along with various measures of structure like the community and hierarchical structures, and the clustering coefficient. Global measures like the shortest path statistics and the self-similarity are also examined.

Spatial tumor immune microenvironment phenotypes in ovarian cancer

Immunotherapy has largely failed in ovarian carcinoma (OC), likely due to that the vast tumor heterogeneity and variation in immune response have hampered clinical trial outcomes. Tumor-immune microenvironment (TIME) profiling may aid in stratification of OC tumors for guiding treatment selection. Here, we used Digital Spatial Profiling combined with image analysis to characterize regions of spatially distinct TIME phenotypes in OC to assess whether immune infiltration pattern can predict presence of immuno-oncology targets. Tumors with diffuse immune infiltration and increased tumor-immune spatial interactions had higher presence of IDO1, PD-L1, PD-1 and Tim-3, while focal immune niches had more CD163 macrophages and a preliminary worse outcome. Immune exclusion was associated with presence of Tregs and Fibronectin. High-grade serous OC showed an overall stronger immune response and presence of multiple targetable checkpoints. Low-grade serous OC was associated with diffuse infiltration and a high expression of STING, while endometrioid OC had higher presence of CTLA-4. Mucinous and clear cell OC were dominated by focal immune clusters and immune-excluded regions, with mucinous tumors displaying T-cell rich immune niches.

Network analysis of meditative states in highly skilled meditators using EEG and horizontal visibility graphs

The benefits of meditation are increasingly recognized, and some forms are now used for clinical intervention. However, the electrophysiological correlates of meditative states are not yet well understood, and the limited predictive accuracy of known markers of meditation suggest that not all information relevant to meditation has been captured by previous work.Here, we convert electroencephalography (EEG) time series into scale-free networks using horizontal visibility graphs (HVGs), which are well-suited to distinguishing deterministic dynamical systems from stochastic systems, allowing them to model novel aspects of cortical oscillatory activity. Based on HVGs, we introduce and evaluate a general class of predictors, which can be used to augment existing features in contemplative neuroscience, and exhibit high predictive power for several types of meditation.We show the statistical significance of these network predictors - and their increased performance compared to popular spectral and non-linear features such as complexity or entropy - on data from highly skilled meditators, in a continuous setting applicable to real-time analysis and applications such as neurofeedback.

Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy

RATIONALE AND OBJECTIVES

The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T1-weighted images.

METHODS

A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation.

RESULTS

In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity.

CONCLUSION

Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers.

Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity

Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.

Using adjusted local assortativity with Molecular Pixelation unveils colocalization of membrane proteins with immunological significance

Advances in spatial proteomics and protein colocalization are a driving force in the understanding of cellular mechanisms and their influence on biological processes. New methods in the field of spatial proteomics call for the development of algorithms and open up new avenues of research. The newly introduced Molecular Pixelation (MPX) provides spatial information on surface proteins and their relationship with each other in single cells. This allows for in silico representation of neighborhoods of membrane proteins as graphs. In order to analyze this new data modality, we adapted local assortativity in networks of MPX single-cell graphs and created a method that is able to capture detailed information on the spatial relationships of proteins. The introduced method can evaluate the pairwise colocalization of proteins and access higher-order similarity to investigate the colocalization of multiple proteins at the same time. We evaluated the method using publicly available MPX datasets where T cells were treated with a chemokine to study uropod formation. We demonstrate that adjusted local assortativity detects the effects of the stimuli at both single- and multiple-marker levels, which enhances our understanding of the uropod formation. We also applied our method to treating cancerous B-cell lines using a therapeutic antibody. With the adjusted local assortativity, we recapitulated the effect of rituximab on the polarity of CD20. Our computational method together with MPX improves our understanding of not only the formation of cell polarity and protein colocalization under stimuli but also advancing the overall insight into immune reaction and reorganization of cell surface proteins, which in turn allows the design of novel therapies. We foresee its applicability to other types of biological spatial data when represented as undirected graphs.

Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.

Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect survival

Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. Here, we have utilized orthogonal imaging mass cytometry with single-cell and spatial transcriptomic approaches to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME). Our results show that different myeloid populations have distinct and reproducible compartmentalization patterns in the GBM TME that is driven by tissue hypoxia, regional chemokine signaling, and varied homotypic and heterotypic cellular interactions. We subsequently identified specific tumor subregions in GBM, based on composition of identified myeloid cell populations, that were linked to patient survival. Our results provide insight into the spatial organization of myeloid cell subpopulations in GBM, and how this is predictive of clinical outcome.

Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data

MOTIVATION

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs.

RESULTS

To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

AVAILABILITY AND IMPLEMENTATION

STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.

Beyond nothingness in the formation and functional relevance of voids in polymer films

Voids-the nothingness-broadly exist within nanomaterials and impact properties ranging from catalysis to mechanical response. However, understanding nanovoids is challenging due to lack of imaging methods with the needed penetration depth and spatial resolution. Here, we integrate electron tomography, morphometry, graph theory and coarse-grained molecular dynamics simulation to study the formation of interconnected nanovoids in polymer films and their impacts on permeance and nanomechanical behaviour. Using polyamide membranes for molecular separation as a representative system, three-dimensional electron tomography at nanometre resolution reveals nanovoid formation from coalescence of oligomers, supported by coarse-grained molecular dynamics simulations. Void analysis provides otherwise inaccessible inputs for accurate fittings of methanol permeance for polyamide membranes. Three-dimensional structural graphs accounting for the tortuous nanovoids within, measure higher apparent moduli with polyamide membranes of higher graph rigidity. Our study elucidates the significance of nanovoids beyond the nothingness, impacting the synthesis‒morphology‒function relationships of complex nanomaterials.

Identifying influential nodes based on the disassortativity and community structure of complex network

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data.

Urban segregation on multilayered transport networks: a random walk approach

We present a novel method for analysing socio-spatial segregation in cities by considering constraints imposed by transportation networks. Using a multilayered network approach, we model the interaction probabilities of socio-economic groups with random walks and Lévy flights. This method allows for evaluation of new transport infrastructure's impact on segregation while quantifying each network's contribution to interaction opportunities. The proposed random walk segregation index measures the probability of individuals encountering diverse social groups based on their available means of transit via random walks. The index incorporates temporal constraints in urban mobility with a parameter, α ∈ [ 0 , 1 ) , of the probability of the random walk continuing at each time step. By applying this to a toy model and conducting a sensitivity analysis, we explore how the index changes dependent on this temporal constraint. When the parameter equals zero, the measure simplifies to an isolation index. When the parameter approaches one it represents the city's overall socio-economic distribution by mirroring the steady-state of the random walk process. Using Cuenca, Ecuador as a case study, we illustrate the method's applicability in transportation planning as a valuable tool for policymakers, addressing spatial distribution of socio-economic groups and the connectivity of existing transport networks, thus promoting equitable interactions throughout the city.

Interplay between tie strength and neighbourhood topology in complex networks

Granovetter's weak ties theory is a very important sociological theory according to which a correlation between edge weight and the network's topology should exist. More specifically, the neighbourhood overlap of two nodes connected by an edge should be positively correlated with edge weight (tie strength). However, some real social networks exhibit a negative correlation-the most prominent example is the scientific collaboration network, for which overlap decreases with edge weight. It has been demonstrated that the aforementioned inconsistency with Granovetter's theory can be alleviated in the scientific collaboration network through the use of asymmetric measures. In this paper, we explain that while asymmetric measures are often necessary to describe complex networks and to confirm Granovetter's theory, their interpretation is not simple, and there are pitfalls that one must be wary of. The definitions of asymmetric weights and overlaps introduce structural correlations that must be filtered out. We show that correlation profiles can be used to overcome this problem. Using this technique, not only do we confirm Granovetter's theory in various real and artificial social networks, but we also show that Granovetter-like weight-topology correlations are present in other complex networks (e.g. metabolic and neural networks). Our results suggest that Granovetter's theory is a sociological manifestation of more general principles governing various types of complex networks.

Stability in social networks

Dunbar's number is the cognitive limit of human beings to maintain stable relationships with other individuals in their social networks, and it is found to be 150. It is based on the neocortex size of humans. Usually, Dunbar's number and related phenomena are studied from the perspective of an individual. Dunbar's number also plays a crucial role in evolutionary psychology and allied areas. However, no study done so far has considered a couple who are in a stable relationship as a system from the perspective of Dunbar's number and its hierarchy layers. In this paper, we study the impact of Dunbar's number and Dunbar's hierarchy from the perspective of a couple by studying mathematically the conjoint Dunbar graphs for a couple. The cost of romance is the loss of almost two people from one's support network when a human being enters into a new relationship. Thus, we obtain mathematically that there is no significant change in one's friendship if human beings spend negligible time with their partners. Also, along with marriage and friendship development, we attempt to assess how a person's social network structure holds up over the course of a romantic relationship. The stability of personal social networks is discussed through soft set theory and balance theoretic approach.

The simpliciality of higher-order networks

Higher-order networks are widely used to describe complex systems in which interactions can involve more than two entities at once. In this paper, we focus on inclusion within higher-order networks, referring to situations where specific entities participate in an interaction, and subsets of those entities also interact with each other. Traditional modeling approaches to higher-order networks tend to either not consider inclusion at all (e.g., hypergraph models) or explicitly assume perfect and complete inclusion (e.g., simplicial complex models). To allow for a more nuanced assessment of inclusion in higher-order networks, we introduce the concept of "simpliciality" and several corresponding measures. Contrary to current modeling practice, we show that empirically observed systems rarely lie at either end of the simpliciality spectrum. In addition, we show that generative models fitted to these datasets struggle to capture their inclusion structure. These findings suggest new modeling directions for the field of higher-order network science.

Transmission matrices used in epidemiologic modelling

Mixing matrices are included in infectious disease models to reflect transmission opportunities between population strata. These matrices were originally constructed on the basis of theoretical considerations and most of the early work in this area originates from research on sexually transferred diseases in the 80s, in response to AIDS. Later work in the 90s populated these matrices on the basis of survey data gathered to capture transmission risks for respiratory diseases. We provide an overview of developments in the construction of matrices for capturing transmission opportunities in populations. Such transmission matrices are useful for epidemiologic modelling to capture within and between stratum transmission and can be informed from theoretical mixing assumptions, informed by empirical evidence gathered through investigation as well as generated on the basis of data. Links to summary measures and threshold conditions are also provided.

Social network analysis of the CaRE2 health equity center: Team science in full display

Cancer health disparities that exist in the Black or African American and Hispanic or Latino/x communities are scientific challenges, yet there are limited team science approaches to mitigate these challenges. This article's purpose is to evaluate the team science collaborations of the National Institutes of Health-funded Florida-California Cancer Research, Education & Engagement (CaRE2 ) Center partnership underscoring the inclusion of multidisciplinary team members and future under-represented minority (URM) cancer researchers. To understand our collaborative efforts, we conducted a social network analysis (SNA) of the CaRE2 Center partnership among University of Florida, Florida A&M University, and University of Southern California with data collected via the dimensions.ai application programming interface. We downloaded metadata for all publications associated with dimensions.ai IDs. The CaRE2 collaboration network increased over time as evidenced by accruing more external collaborators and more publishing of collaborative works. Degree centrality of key personnel was stable in each wave of the networks. CaRE2 key personnel averaged a total of 60.8 collaborators in 2018-2019 (SD = 57.4, minimum = 3, maximum = 221), and 65.8 collaborators in 2020-2021 (SD = 56.06, minimum = 4, maximum = 222). Betweenness was largely stable across all groups and waves. We observed a steady decline in transitivity, the probability that a pair of CaRE2 co-authors shared a third co-author, from 0.74 in 2018 to 0.47 in 2022. The SNA findings suggest that the CaRE2 Center partnership's publications show growth in team science collaborations with the inclusion of multidisciplinary team members from the three partner institutions and future URM cancer researchers who were mentored as trainees and early-stage investigators.

Assortative mixing of opinions about COVID-19 vaccination in personal networks

Many countries worldwide had difficulties reaching a sufficiently high vaccination uptake during the COVID-19 pandemic. Given this context, we collected data from a panel of 30,000 individuals, which were representative of the population of Romania (a country in Eastern Europe with a low 42.6% vaccination rate) to determine whether people are more likely to be connected to peers displaying similar opinions about COVID-19 vaccination. We extracted 443 personal networks, amounting to 4430 alters. We estimated multilevel logistic regression models with random-ego-level intercepts to predict individual opinions about COVID-19 vaccination. Our evidence indicates positive opinions about the COVID-19 vaccination cluster. Namely, the likelihood of having a positive opinion about COVID-19 vaccination increases when peers have, on average, a more positive attitude than the rest of the nodes in the network (OR 1.31, p < 0.001). We also found that individuals with higher education and age are more likely to hold a positive opinion about COVID-19 vaccination. With the given empirical data, our study cannot reveal whether this assortative mixing of opinions is due to social influence or social selection. However, it may nevertheless have implications for public health interventions, especially in countries that strive to reach higher uptake rates. Understanding opinions about vaccination can act as an early warning system for potential outbreaks, inform predictions about vaccination uptake, or help supply chain management for vaccine distribution.

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors - one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled - and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.