Mixing patterns in networks

Homophily and polarization on political twitter during the 2017 Norwegian election

Ideological homophily on social media has been receiving increased scholarly interest, as it is associated with the formation of filter bubbles, echo chambers, and increased ideological polarization. And yet, no linkage necessarily exists between ideological homophily, echo chambers, and polarization. Despite political interactions on social media taking place to a large extent between like-minded individuals, cross-cutting interactions are also frequent. Using Twitter data, we investigated the extent to which ideological homophily, echo chambers, and polarization occur together and characterize the network of political Twitter users during the 2017 election in Norway. Despite the presence of some degree of ideological homophily, we did not find evidence of echo chambers in the Norwegian political Twittersphere during the 2017 election. And yet, the retweet network is characterized by a significant degree of polarization across ideological blocs. Our findings support the thesis according to which polarization on social media may have drivers other than the technological deterministic effect of social media affordances enhancing the formation of online echo chambers.

Analysis of cattle movement networks in Paraguay: Implications for the spread and control of infectious diseases

Beef exports represent a substantial part of Paraguay's agricultural sector. Cattle movements involve a high risk due to the possible spread of bovine diseases that can have a significant impact on the country's economy. We analyzed cattle movements from 2014 to 2018 using the networks analysis methodology at the holding and district levels at different temporal scales. We built two types of networks to identify network characteristics that may contribute to the spread of two diseases with different epidemiological characteristics: i) a network including all cattle movements to consider the transmission of a disease of rapid spread like foot and mouth disease, and ii) a network including only cow movements to account for bovine brucellosis, a disease of slow spread that occurs mainly in adult females. Network indicators did not vary substantially among the cattle and cow only networks. The holdings/districts included in the largest strongly connected components were distributed throughout the country. Percolation analysis performed at the holding level showed that a large number of holdings should be removed to make the largest strongly connected component disappear. Higher values of the centrality indicators were found for markets than for farms, indicating that they may play an important role in the spread of an infectious disease. At the holding level (but not at the district level), the networks exhibited characteristics of small-world networks. This property may facilitate the spread of foot and mouth disease in case of re-emergence, or of bovine brucellosis in the country through cattle movements. They should be taken into account when implementing surveillance or control measures for these diseases.

Network analyses unveil ageing-associated pathways evolutionarily conserved from fungi to animals

The genetic roots of the diverse paces and shapes of ageing and of the large variations in longevity observed across the tree of life are poorly understood. Indeed, pathways associated with ageing/longevity are incompletely known, both in terms of their constitutive genes/proteins and of their molecular interactions. Moreover, there is limited overlap between the genes constituting these pathways across mammals. Yet, dedicated comparative analyses might still unravel evolutionarily conserved, important pathways associated with longevity or ageing. Here, we used an original strategy with a double evolutionary and systemic focus to analyse protein interactions associated with ageing or longevity during the evolution of five species of Opisthokonta. We ranked these proteins and interactions based on their evolutionary conservation and centrality in past and present protein-protein interaction (PPI) networks, providing a big systemic picture of the evolution of ageing and longevity pathways that identified which pathways emerged in which Opisthokonta lineages, were conserved, and/or central. We confirmed that longevity/ageing-associated proteins (LAPs), be they pro- or anti-longevity, are highly central in extant PPI, consistently with the antagonistic pleiotropy theory of ageing, and identified key antagonistic regulators of ageing/longevity, 52 of which with homologues in humans. While some highly central LAPs were evolutionarily conserved for over a billion years, we report a clear transition in the functionally important components of ageing/longevity within bilaterians. We also predicted 487 novel evolutionarily conserved LAPs in humans, 54% of which are more central than mTOR, and 138 of which are druggable, defining new potential targets for anti-ageing treatments in humans.

Transport equipment network analysis: the value-added contribution

Emerging in the twenty-first century, Network Science provides practical measures to interpret a system's interactions between the components and their links. Literature has focused on countries' interconnections on the final goods, but its application on the value-added from a network perspective in trade is still imitated. This paper applies network science properties and a multi-regional input-output analysis by using the UNCTAD-Eora Global Value Chain Database on the Transport Equipment value added on 2017 to unwrap the specific structural characteristics of the industry. Results show that the industry is highly centralized. The center of the network is dominated by developed countries, mainly from Europe, the United States, and Japan. Emerging countries such as China, Mexico, Thailand, and Poland also have an important position. In addition, the structure reveals two sub-hubs located in East Europe and North America. By extending to community detection, the network consists of three different communities led by Germany, the United States, and the United Kingdom, associated with more significant value-added flows. The study concludes that flows are not always consistent with the economy's geographical location as usually final goods analysis suggests, and highlight the need to continue using the complex network to reveal the world trade structure.

Quantum physics in connected worlds

Theoretical research into many-body quantum systems has mostly focused on regular structures which have a small, simple unit cell and where a vanishingly small fraction of the pairs of the constituents directly interact. Motivated by advances in control over the pairwise interactions in many-body simulators, we determine the fate of spin systems on more general, arbitrary graphs. Placing the minimum possible constraints on the underlying graph, we prove how, with certainty in the thermodynamic limit, such systems behave like a single collective spin. We thus understand the emergence of complex many-body physics as dependent on 'exceptional', geometrically constrained structures such as the low-dimensional, regular ones found in nature. Within the space of dense graphs we identify hitherto unknown exceptions via their inhomogeneity and observe how complexity is heralded in these systems by entanglement and highly non-uniform correlation functions. Our work paves the way for the discovery and exploitation of a whole class of geometries which can host uniquely complex phases of matter.

Disrupting drive-by download networks on Twitter

This paper tests disruption strategies in Twitter networks containing malicious URLs used in drive-by download attacks. Cybercriminals use popular events that attract a large number of Twitter users to infect and propagate malware by using trending hashtags and creating misleading tweets to lure users to malicious webpages. Due to Twitter’s 280 character restriction and automatic shortening of URLs, it is particularly susceptible to the propagation of malware involved in drive-by download attacks. Considering the number of online users and the network formed by retweeting a tweet, a cybercriminal can infect millions of users in a short period. Policymakers and researchers have struggled to develop an efficient network disruption strategy to stop malware propagation effectively. We define an efficient strategy as one that considers network topology and dependency on network resilience, where resilience is the ability of the network to continue to disseminate information even when users are removed from it. One of the challenges faced while curbing malware propagation on online social platforms is understanding the cybercriminal network spreading the malware. Combining computational modelling and social network analysis, we identify the most effective strategy for disrupting networks of malicious URLs. Our results emphasise the importance of specific network disruption parameters such as network and emotion features, which have proved to be more effective in disrupting malicious networks compared to random strategies. In conclusion, disruption strategies force cybercriminal networks to become more vulnerable by strategically removing malicious users, which causes successful network disruption to become a long-term effort.

$$\Delta $$-Conformity: multi-scale node assortativity in feature-rich stream graphs

Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the time-varying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: $$\Delta $$ Δ -Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes’ connectivity along a period of time $$\Delta $$ Δ starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce $$\Delta $$ Δ -Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.

Effect of Levodopa Medication on Human Brain Connectome in Parkinson's Disease-A Combined Graph Theory and EEG Study

Background. Levodopa-based drugs are widely used for mitigating the complications induced by Parkinson's disease (PD). Despite the positive effects, several issues regarding the way that levodopa changes brain activities have remained unclear. Methods. A combined strategy using EEG data and graph theory was used for investigating how levodopa changed connectome and processing hubs of the brain during resting-state. Obtained results were subjected to ANOVA test and multiple-comparison post-hoc correction procedure. Results. Outcomes showed that graph topology was not significantly different between PD and healthy groups during the eyes-closed condition, while in the eyes-open condition, statistically significant differences were found. The main effect of levodopa medication was observed for gamma-band activity in which levodopa changed the brain connectome toward a star-like topology. Considering the beta subband of EEG data, graph leaf number increased following levodopa medication in PD patients. Enhanced brain connectivity in the gamma band and reduced beta band connections in the basal ganglia were also observed after levodopa medication. Furthermore, source localization using dipole fitting showed that levodopa suppressed the activity of collateral trigone. Conclusion. Our combined EEG and graph analysis showed that levodopa medication changed the brain connectome, especially in the high-frequency range of brain electrical activities (beta and gamma).

QEEG Biomarkers for ECT Treatment Response in Schizophrenia

Background: Electroconvulsive therapy (ECT) is a clinically effective treatment for schizophrenia (SZD). However, studies have shown that only about 50 to 80% of patients show response to ECT. To identify the most suitable patients for ECT, developing biomarkers predicting ECT response remains an important goal. This study aimed to explore the quantitative electroencephalography (QEEG) biomarkers to predict ECT efficacy. Methods: Thirty patients who met DSM-5 criteria for SZD and had been assigned to ECT were recruited. 32-lead Resting-EEG recordings were collected one hour before the initial ECT treatment. Positive and negative symptoms scale (PANSS) was assessed at baseline and after the eighth ECT session. EEG data were analyzed using mutual information. Results: In the brain network density threshold range of 0.05 to 0.2, the assortativity of the right temporal, right parietal, and right occipital cortex in the response group was significantly higher than that in the non-response group (p < .05) in the beta band. In the theta band, the left frontal, parietal, right occipital cortex, and central area assortativity were higher in the response group than in the non-response group (p < .05). Conclusions: QEEG might be a useful approach to identify the candidate biomarker for ECT in clinical practice.

Identifying accurate link predictors based on assortativity of complex networks

Link prediction algorithms in complex networks, such as social networks, biological networks, drug-drug interactions, communication networks, and so on, assign scores to predict potential links between two nodes. Link prediction (LP) enables researchers to learn unknown, new as well as future interactions among the entities being modeled in the complex networks. In addition to measures like degree distribution, clustering coefficient, centrality, etc., another metric to characterize structural properties is network assortativity which measures the tendency of nodes to connect with similar nodes. In this paper, we explore metrics that effectively predict the links based on the assortativity profiles of the complex networks. To this end, we first propose an approach that generates networks of varying assortativity levels and utilize three sets of link prediction models combining the similarity of neighborhoods and preferential attachment. We carry out experiments to study the LP accuracy (measured in terms of area under the precision-recall curve) of the link predictors individually and in combination with other baseline measures. Our analysis shows that link prediction models that explore a large neighborhood around nodes of interest, such as CH2-L2 and CH2-L3, perform consistently for assortative as well as disassortative networks. While common neighbor-based local measures are effective for assortative networks, our proposed combination of common neighbors with node degree is a good choice for the LP metric in disassortative networks. We discuss how this analysis helps achieve the best-parameterized combination of link prediction models and its significance in the context of link prediction from incomplete social and biological network data.

The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database

The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.

Quantifying ethnic segregation in cities through random walks

Socioeconomic segregation has an important role in the emergence of large-scale inequalities in urban areas. Most of the available measures of spatial segregation depend on the scale and size of the system under study, or neglect large-scale spatial correlations, or rely on ad-hoc parameters, making it hard to compare different systems on equal grounds. We propose here a family of non-parametric measures for spatial distributions, based on the statistics of the trajectories of random walks on graphs associated to a spatial system. These quantities provide a consistent estimation of segregation in synthetic spatial patterns, and we use them to analyse the ethnic segregation of metropolitan areas in the US and the UK. We show that the spatial diversity of ethnic distributions, as measured through diffusion on graphs, allow us to compare the ethnic segregation of urban areas having different size, shape, or peculiar microscopic characteristics, and exhibits a strong association with socio-economic deprivation.

Socioeconomic segregation is one of the main factors behind large-scale inequalities in urban areas and its characterisation remains challenging. The authors propose a family of non-parametric measures to quantify spatial heterogeneity through diffusion, and show how this relates to segregation and deprivation

Tracing and testing multiple generations of contacts to COVID-19 cases: cost-benefit trade-offs

Traditional contact tracing tests the direct contacts of those who test positive. But, by the time an infected individual is tested, the infection starting from the person may have infected a chain of individuals. Hence, why should the testing stop at direct contacts, and not test secondary, tertiary contacts or even contacts further down? One deterrent in testing long chains of individuals right away may be that it substantially increases the testing load, or does it? We investigate the costs and benefits of such multi-hop contact tracing for different number of hops. Considering diverse contact networks, we show that the cost-benefit trade-off can be characterized in terms of a single measurable attribute, the initial epidemic growth rate. Once this growth rate crosses a threshold, multi-hop contact tracing substantially reduces the outbreak size compared with traditional tracing. Multi-hop even incurs a lower cost compared with the traditional tracing for a large range of values of the growth rate. The cost-benefit trade-offs can be classified into three phases depending on the value of the growth rate. The need for choosing a larger number of hops becomes greater as the growth rate increases or the environment becomes less conducive toward containing the disease.

Spatial clustering in vaccination hesitancy: The role of social influence and social selection

The phenomenon of vaccine hesitancy behavior has gained ground over the last three decades, jeopardizing the maintenance of herd immunity. This behavior tends to cluster spatially, creating pockets of unprotected sub-populations that can be hotspots for outbreak emergence. What remains less understood are the social mechanisms that can give rise to spatial clustering in vaccination behavior, particularly at the landscape scale. We focus on the presence of spatial clustering, and aim to mechanistically understand how different social processes can give rise to this phenomenon. In particular, we propose two hypotheses to explain the presence of spatial clustering: (i) social selection, in which vaccine-hesitant individuals share socio-demographic traits, and clustering of these traits generates spatial clustering in vaccine hesitancy; and (ii) social influence, in which hesitant behavior is contagious and spreads through neighboring societies, leading to hesitant clusters. Adopting a theoretical spatial network approach, we explore the role of these two processes in generating patterns of spatial clustering in vaccination behaviors under a range of spatial structures. We find that both processes are independently capable of generating spatial clustering, and the more spatially structured the social dynamics in a society are, the higher spatial clustering in vaccine-hesitant behavior it realizes. Together, we demonstrate that these processes result in unique spatial configurations of hesitant clusters, and we validate our models of both processes with fine-grain empirical data on vaccine hesitancy, social determinants, and social connectivity in the US. Finally, we propose, and evaluate the effectiveness of two novel intervention strategies to diminish hesitant behavior. Our generative modeling approach informed by unique empirical data provides insights on the role of complex social processes in driving spatial heterogeneity in vaccine hesitancy.

The structural change and influencing factors of carbon transfer network in global value chains

Based on the gross trade accounting method and WIOD data, this paper quantifies the global carbon transfer from 2000 to 2014. The social network analysis and temporal exponential random graph model (TERGM) are employed to investigate the structural change and influencing factors of global carbon transfer network from a global value chains (GVCs) perspective. Results show that the deepening of the GVC division connection has enhanced the relations in global carbon transfer network, and the low reciprocity and high disassortativity lie in the difference in GVC division positions among economies. Block model analysis manifests that Plate Ⅱ acts as the engine plate in the network which consists of China and the other four economies. The GVC division connection and relative GVC division position significantly facilitate the formation of carbon emission transfer relations. Moreover, economies' attribute effects and endogenous network structure effects are non-negligible important determinants of network formation.

Social responses to the natural loss of individuals in Barbary macaques

In recent years, there has been considerable interest in investigating how animal social structure is affected by the loss of individuals. This is often achieved using simulations that generate predictions regarding how the removal of ‘key’ individuals from a group affects network structure. However, little is known about the effects of such removals in wild and free-ranging populations, particularly the extent to which naturally occurring mortality events and the loss of a large proportion of individuals from a social group affects the overall structure of a social network. Here, we used data from a population of wild Barbary macaques (Macaca sylvanus) that was exposed to an exceptionally harsh winter, culminating in the death of 64% of the adults from two groups. We analysed how social interaction patterns among surviving individuals were affected by the natural loss of group members using social networks based on affiliative (i.e., grooming) and aggressive social interactions. We show that only the structure of the pre-decline grooming networks was conserved in the post-decline networks, suggesting that grooming, but not aggression networks are resilient against the loss of group members. Surviving group members were not significantly different from the non-survivors in terms of their affiliative and agonistic relationships, and did not form assorted communities in the pre-decline networks. Overall, our results suggest that in primates, patterns of affiliative interactions are more resilient to changes in group composition than aggressive interaction patterns, which tend to be used more flexibly in new conditions.

Synergistic epidemic spreading in correlated networks

We investigate the effect of degree correlation on a susceptible-infected-susceptible (SIS) model with a nonlinear cooperative effect (synergy) in infectious transmissions. In a mean-field treatment of the synergistic SIS model on a bimodal network with tunable degree correlation, we identify a discontinuous transition that is independent of the degree correlation strength unless the synergy is absent or extremely weak. Regardless of synergy (absent or present), a positive and negative degree correlation in the model reduces and raises the epidemic threshold, respectively. For networks with a strongly positive degree correlation, the mean-field treatment predicts the emergence of two discontinuous jumps in the steady-state infected density. To test the mean-field treatment, we provide approximate master equations of the present model. We quantitatively confirm that the approximate master equations agree with not only all qualitative predictions of the mean-field treatment but also corresponding Monte Carlo simulations.

Social media behavior is associated with vaccine hesitancy

Understanding how vaccine hesitancy relates to online behavior is crucial for addressing current and future disease outbreaks. We combined survey data measuring attitudes toward the COVID-19 vaccine with Twitter data in two studies (N 1 = 464 Twitter users, N 2 = 1,600 Twitter users) with preregistered hypotheses to examine how real-world social media behavior is associated with vaccine hesitancy in the United States (US) and the United Kingdom (UK). In Study 1, we found that following the accounts of US Republican politicians or hyper-partisan/low-quality news sites were associated with lower confidence in the COVID-19 vaccine-even when controlling for key demographics such as self-reported political ideology and education. US right-wing influencers (e.g. Candace Owens, Tucker Carlson) had followers with the lowest confidence in the vaccine. Network analysis revealed that participants who were low and high in vaccine confidence separated into two distinct communities (or "echo chambers"), and centrality in the more right-wing community was associated with vaccine hesitancy in the US, but not in the UK. In Study 2, we found that one's likelihood of not getting the vaccine was associated with retweeting and favoriting low-quality news websites on Twitter. Altogether, we show that vaccine hesitancy is associated with following, sharing, and interacting with low-quality information online, as well as centrality within a conservative-leaning online community in the US. These results illustrate the potential challenges of encouraging vaccine uptake in a polarized social media environment.

Self-induced consensus of Reddit users to characterise the GameStop short squeeze

The short squeeze of GameStop (GME) shares in mid-January 2021 has been primarily orchestrated by retail investors of the Reddit r/wallstreetbets community. As such, it represents a paramount example of collective coordination action on social media, resulting in large-scale consensus formation and significant market impact. In this work we characterise the structure and time evolution of Reddit conversation data, showing that the occurrence and sentiment of GME-related comments (representing how much users are engaged with GME) increased significantly much before the short squeeze actually took place. Taking inspiration from these early warnings as well as evidence from previous literature, we introduce a model of opinion dynamics where user engagement can trigger a self-reinforcing mechanism leading to the emergence of consensus, which in this particular case is associated to the success of the short squeeze operation. Analytical solutions and model simulations on interaction networks of Reddit users feature a phase transition from heterogeneous to homogeneous opinions as engagement grows, which we qualitatively compare to the sudden hike of GME stock price. Although the model cannot be validated with available data, it offers a possible and minimal interpretation for the increasingly important phenomenon of self-organized collective actions taking place on social networks.

Identifying HIV-1 Transmission Clusters in Uzbekistan through Analysis of Molecular Surveillance Data

The CRF02_AG and sub-subtype A6 are currently the predominant HIV-1 variants in the Republic of Uzbekistan, but little is known about their time-spatial clustering patterns in high-risk populations. We have applied molecular evolution methods and network analyses to better understand the transmission patterns of these subtypes by analyzing 316 pol sequences obtained during the surveillance study of HIV drug resistance. Network analysis showed that about one third of the HIV infected persons were organized into clusters, including large clusters with more than 35 members. These clusters were composed mostly of injecting drug users and/or heterosexuals, with women having mainly high centrality within networks identified in both subtypes. Phylogenetic analyses of the 'Uzbek' sequences, including those publicly available, show that Russia and Ukraine played a role as the main sources of the current subtype A6 epidemic in the Republic. At the same time, Uzbekistan has been a local center of the CRF02_AG epidemic spread in the former USSR since the early 2000s. Both of these HIV-1 variants continue to spread in Uzbekistan, highlighting the importance of identifying transmission networks and transmission clusters to prevent further HIV spread, and the need for HIV prevention and education campaigns in high-risk groups.

The network signature of constellation line figures

In traditional astronomies across the world, groups of stars in the night sky were linked into constellations-symbolic representations rich in meaning and with practical roles. In some sky cultures, constellations are represented as line (or connect-the-dot) figures, which are spatial networks drawn over the fixed background of stars. We analyse 1802 line figures from 56 sky cultures spanning all continents, in terms of their network, spatial, and brightness features, and ask what associations exist between these visual features and culture type or sky region. First, an embedded map of constellations is learnt, to show clusters of line figures. We then form the network of constellations (as linked by their similarity), to study how similar cultures are by computing their assortativity (or homophily) over the network. Finally, we measure the diversity (or entropy) index for the set of constellations drawn per sky region. Our results show distinct types of line figures, and that many folk astronomies with oral traditions have widespread similarities in constellation design, which do not align with cultural ancestry. In a minority of sky regions, certain line designs appear universal, but this is not the norm: in the majority of sky regions, the line geometries are diverse.

Exploring the role of interpersonal contexts in peer relationships among autistic and non-autistic youth in integrated education

The double empathy problem theory posits that autistic social difficulties emerge from an interpersonal misalignment in social experiences and expectations between autistic and non-autistic people. Supporting this, emerging research reveals better social outcomes in interactions within than across neurotypes among autistic and non-autistic people, emphasizing the need to examine the role of the interpersonal context in autistic social outcomes. However, research on peer relationships among autistic youth primarily focuses on individual characteristics in isolation from the interpersonal context. To address this, this preliminary study explored the effects of student-peer neurotype match on peer relationships among autistic and non-autistic youth in an integrated educational setting. We plotted the peer relationship networks among youth in a school club based on systematic observations of peer interactions over eight 45-min sessions. Descriptive network statistics (node degree and strength) showed that both autistic and non-autistic youth had more and stronger peer relationships with their same- than cross-neurotype peers. Assortativity coefficients revealed a tendency for youth to connect with peers of the same neurotype, rather than with peers with similar social popularity or activity. We further modeled the effects of student-peer neurotype match on peer relationships using exponential random graph models. The findings suggested that student-peer neurotype match predicted the total strength of peer relationships above and beyond the effects of student neurotype, individual heterogeneity in social popularity and activity, and the tendency of mutuality in social relationships. We discussed the strengths and limitations of this study and the implications for future research and inclusion practice.

Eco-evolutionary model on spatial graphs reveals how habitat structure affects phenotypic differentiation

Differentiation mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. Here, we investigate how habitat connectivity and habitat heterogeneity affect phenotypic differentiation by formulating a stochastic eco-evolutionary model where individuals are structured over a spatial graph. We combine analytical insights into the eco-evolutionary dynamics with numerical simulations to understand how the graph topology and the spatial distribution of habitat types affect differentiation. We show that not only low connectivity but also heterogeneity in connectivity promotes neutral differentiation, due to increased competition in highly connected vertices. Habitat assortativity, a measure of habitat spatial auto-correlation in graphs, additionally drives differentiation under habitat-dependent selection. While assortative graphs systematically amplify adaptive differentiation, they can foster or depress neutral differentiation depending on the migration regime. By formalising the eco-evolutionary and spatial dynamics of biological populations on graphs, our study establishes fundamental links between landscape features and phenotypic differentiation.

Clustering in a newly forming social network by subjective perceptions of loneliness

Objectives: To determine whether first-year college students cluster in networks based on subjective perceptions of loneliness. Participants: 492 first-year Notre Dame students completed surveys across two semesters and provided communication data used to reconstruct their social networks. Methods: Subjective perceptions of loneliness are measured using the Social and Emotional Loneliness Scale for Adults (SELSA). Correlations between an individual's loneliness and the average loneliness of their alters are compared to associations in random networks created using a rewiring algorithm to determine statistical significance. Results: During their first semester, students are more likely than chance to form ties with other students with similar levels of family and romantic loneliness. In their second semester, students cluster on romantic loneliness but not on family or social loneliness. Conclusions: Students are more likely than chance to form ties with people with similar self-perceived levels of loneliness, but only for certain types of loneliness and during certain periods.

A Census of Human Methionine-Rich Prion-like Domain-Containing Proteins

Methionine-rich prion-like proteins can regulate liquid–liquid phase separation processes in response to stresses. To date, however, very few proteins have been identified as methionine-rich prion-like. Herein, we have performed a computational survey of the human proteome to search for methionine-rich prion-like domains. We present a census of 51 manually curated methionine-rich prion-like proteins. Our results show that these proteins tend to be modular in nature, with molecular sizes significantly greater than those we would expect due to random sampling effects. These proteins also exhibit a remarkably high degree of spatial compaction when compared to average human proteins, even when protein size is accounted for. Computational evidence suggests that such a high degree of compactness might be due to the aggregation of methionine residues, pointing to a potential redox regulation of compactness. Gene ontology and network analyses, performed to shed light on the biological processes in which these proteins might participate, indicate that methionine-rich and non-methionine-rich prion-like proteins share gene ontology terms related to the regulation of transcription and translation but, more interestingly, these analyses also reveal that proteins from the methionine-rich group tend to share more gene ontology terms among them than they do with their non-methionine-rich prion-like counterparts.

A Graph-Cut-Based Approach to Community Detection in Networks

Networks can be used to model various aspects of our lives as well as relations among many real-world entities and objects. To detect a community structure in a network can enhance our understanding of the characteristics, properties, and inner workings of the network. Therefore, there has been significant research on detecting and evaluating community structures in networks. Many fields, including social sciences, biology, engineering, computer science, and applied mathematics, have developed various methods for analyzing and detecting community structures in networks. In this paper, a new community detection algorithm, which repeats the process of dividing a community into two smaller communities by finding a minimum cut, is proposed. The proposed algorithm is applied to some example network data and shows fairly good community detection results with comparable modularity Q values.

Connections matter: a proxy measure for evaluating network membership with an application to the Seventh Research Framework Programme

Although the topic of networks has received significant attention from the scientific literature, it remains to be seen whether it is possible to quantify the degree to which an organisation benefits from being part of a network. Starting from the concept of network value and that of Metcalfe’s Law, this paper introduces and defines the collective network effect (CNE). CNE is based on the concept that a network member is not only affected by its friends but also by the friends of its friends. By taking into account network connection patterns, CNE provides a proxy for quantifying the benefit of network membership. We computed the CNE for the nodes of a large network built using the whole set of common projects among the participants of the 7th Framework Programme for Research and Technological Development of the European Commission. The obtained results show that nodes with a higher CNE have access to substantially more conspicuous fundings than nodes with a lower CNE. In general, such a measure could supplement other centrality measures and be useful for organisations and companies aiming to evaluate both their current situation and the potential partners they should link with in order to extract the highest benefits from network membership.

Human papillomavirus vaccine coverage in male-male partnerships attending a sexual health clinic in Melbourne, Australia

We aimed to investigate the sexual mixing by human papillomavirus (HPV) vaccination status in male-male partnerships and estimate the proportion of male-male partnerships protected against HPV. We analyzed male-male partnerships attending the Melbourne Sexual Health Center between 2018 and 2019. Data on self-reported HPV vaccination status were collected. Newman’s assortativity coefficient was used to examine the sexual mixing by HPV vaccination status. Assortativity refers to the tendency of individuals to have partners with similar characteristics (i.e. same vaccination status). Of 321 male-male partnerships where both men reported their HPV vaccination status, 52.6% (95% CI: 47.0–58.2%) partnerships had both men vaccinated, 32.1% (95% CI: 27.0–37.5%) partnerships had only one man vaccinated, and 15.3% (95% CI: 11.5–19.7%) had both men unvaccinated. The assortativity on HPV vaccination status was moderate (assortativity coefficient = 0.265, 95% CI: 0.196–0.335). There were about 15% of male-male partnerships where both men were not protected against HPV. Interventions targeting vaccinated individuals to encourage their unvaccinated partners to be vaccinated might increase the HPV vaccine coverage.

A novel method for assessing and measuring homophily in networks through second-order statistics

We present a new method for assessing and measuring homophily in networks whose nodes have categorical attributes, namely when the nodes of networks come partitioned into classes (colors). We probe this method in two different classes of networks: (i) protein-protein interaction (PPI) networks, where nodes correspond to proteins, partitioned according to their functional role, and edges represent functional interactions between proteins (ii) Pokec on-line social network, where nodes correspond to users, partitioned according to their age, and edges respresent friendship between users.Similarly to other classical and well consolidated approaches, our method compares the relative edge density of the subgraphs induced by each class with the corresponding expected relative edge density under a null model. The novelty of our approach consists in prescribing an endogenous null model, namely, the sample space of the null model is built on the input network itself. This allows us to give exact explicit expression for the [Formula: see text]-score of the relative edge density of each class as well as other related statistics. The [Formula: see text]-scores directly quantify the statistical significance of the observed homophily via Čebyšëv inequality. The expression of each [Formula: see text]-score is entered by the network structure through basic combinatorial invariant such as the number of subgraphs with two spanning edges. Each [Formula: see text]-score is computed in [Formula: see text] time for a network with n nodes and m edges. This leads to an overall efficient computational method for assesing homophily. We complement the analysis of homophily/heterophily by considering [Formula: see text]-scores of the number of isolated nodes in the subgraphs induced by each class, that are computed in O(nm) time. Theoretical results are then exploited to show that, as expected, both the analyzed network classes are significantly homophilic with respect to the considered node properties.

Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability

Giant urban rail transit (GURT) systems have been formed in many metropolises and play a critical role in addressing serious traffic congestion. Unfortunately, as a dynamic and complex system, the vulnerability of GURT networks under various failure scenarios will be more prominent as the network expansion continues. Thus, it is imperative to explore the complex structural characteristics of the network and improve the ability to deal with the disturbance of emergencies. In this study, the destruction resistance of GURT networks with scale growth is illustrated from a vulnerability perspective. Specifically, taking Shanghai rail transit (SHRT) system as an example, the network topology model is constructed using the Space L method, and the network structure characteristics are analyzed based on the complex network theory. In addition, five attack strategies are developed to represent random and targeted attacks during the simulation of network failure, and two metrics are determined to evaluate the network vulnerability. Some meaningful results have been obtained: (i) The Shanghai rail transit planning network (SHRTPN) has increased the network efficiency by more than 10% over the Shanghai rail transit operating network (SHRTON) and has effectively enhanced the network destruction resistance. (ii) The SHRT network is a small-world network and shows significant vulnerability under the targeted attacks. The failure of only 3% high betweenness stations in SHRTON can lead to a 66.2% decrease in the network efficiency and a 75.8% decrease in the largest connected component (LCC) ratio. (iii) Attacking stations will cause more severe network failures than attacking edges, and it is necessary to focus on preventing catastrophic network failure caused by the critical station’s failure breaking the threshold. Finally, the strategies for improving the destruction resistance of GURT networks are proposed. The findings of this research can provide an essential reference for the rational planning, safety protection, and sustainable construction of GURT systems.

Role of assortativity in predicting burst synchronization using echo state network

In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons. Specifically, we investigate the ability of ESN in predicting the burst synchronization of an ensemble of Rulkov neurons placed on a scale-free network. We have shown that a limited number of nodal dynamics used as input in the machine can capture the real trend of burst synchronization in this network. Further, we investigate the proper selection of nodal inputs of degree-degree (positive and negative) correlated networks. We show that for a disassortative network, selection of different input nodes based on degree has no significant role in the machine's prediction. However, in the case of assortative network, training the machine with the information (i.e., time series) of low degree nodes gives better results in predicting the burst synchronization. The results are found to be consistent with the investigation carried out with a continuous time Hindmarsh-Rose neuron model. Furthermore, the role of hyperparameters like spectral radius and leaking parameter of ESN on the prediction process has been examined. Finally, we explain the underlying mechanism responsible for observing these differences in the prediction in a degree correlated network.

Topology Analysis of Natural Gas Pipeline Networks Based on Complex Network Theory

With the improvement of natural gas network interconnection, the network topology becomes increasingly complex. The significance of analyzing topology is gradually becoming prominent, and a systematic analysis method is required. This paper selects two typical natural gas pipeline networks: one in Europe, and the other in North China. Based on complex network theory and the nature of natural gas pipelines, topological models for the two typical networks were established and the evaluation indexes were developed based on four factors: network type, overall topological structure characteristics, path-related topological structure characteristics, and topological structure robustness. Using these indexes, the topological structure of the two typical networks is compared and analyzed quantitatively. The comparison shows that the European network topology has more redundancy, higher transmission efficiency, and greater robustness. The topology analysis method proposed in this paper is practical and suitable for the preliminary analysis of natural gas pipeline networks. The conclusions achieved by this method can assist operators in gaining an intuitive understanding of the overall characteristics, robustness, and key features of pipeline network topology, which is useful in the implementation of hierarchical prevention and control. It also serves as a solid theoretical foundation and guidance for network expansion, interconnection construction, and precise hydraulic simulation calculation in the next stage.

A network approach to decipher the dynamics of Lysobacteraceae plasmid gene sharing

Plasmids provide an efficient vehicle for gene sharing among bacterial populations, playing a key role in bacterial evolution. Network approaches are particularly suitable to represent multipartite relationships and are useful tools to characterize plasmid-mediated gene sharing events. The Lysobacteraceae bacterial family gathers plant commensal, plant pathogenic and opportunistic human pathogens for which plasmid mediated adaptation was reported. We searched for homologues of plasmid gene sequences from this family in all the diversity of available bacterial genome sequences and built a network of plasmid gene sharing from the results. While plasmid genes are openly shared between the bacteria of the Lysobacteraceae family, taxonomy strongly defined the boundaries of these exchanges, that only barely reached other families. Most inferred plasmid gene sharing events involved a few genes only, and evidence of full plasmid transfers were restricted to taxonomically close taxon. We detected multiple plasmid-chromosome gene transfers, among which the otherwise known sharing of a heavy metal resistance transposon. In the network, bacterial lifestyles shaped sub-structures of isolates colonizing specific ecological niches and harboring specific types of resistance genes. Genes associated to pathogenicity or antibiotic and metal resistance were among those that most importantly structured the network, highlighting the imprints of human-mediated selective pressure on pathogenic populations. A massive sequencing effort on environmental Lysobacteraceae is therefore required to refine our understanding on how this reservoir fuels the emergence and the spread of genes amongst this family and its potential impact on plant, animal and human health.

Social synchronization of brain activity increases during eye-contact

Humans make eye-contact to extract information about other people’s mental states, recruiting dedicated brain networks that process information about the self and others. Recent studies show that eye-contact increases the synchronization between two brains but do not consider its effects on activity within single brains. Here we investigate how eye-contact affects the frequency and direction of the synchronization within and between two brains and the corresponding network characteristics. We also evaluate the functional relevance of eye-contact networks by comparing inter- and intra-brain networks of friends vs. strangers and the direction of synchronization between leaders and followers. We show that eye-contact increases higher inter- and intra-brain synchronization in the gamma frequency band. Network analysis reveals that some brain areas serve as hubs linking within- and between-brain networks. During eye-contact, friends show higher inter-brain synchronization than strangers. Dyads with clear leader/follower roles demonstrate higher synchronization from leader to follower in the alpha frequency band. Importantly, eye-contact affects synchronization between brains more than within brains, demonstrating that eye-contact is an inherently social signal. Future work should elucidate the causal mechanisms behind eye-contact induced synchronization.

Friends making eye-contact have higher inter-brain synchronization than strangers. Eye-contact affects neural synchronization between brains more than within a brain, highlighting that eye-contact is an inherently social signal.

Hypergraph assortativity: A dynamical systems perspective

The largest eigenvalue of the matrix describing a network's contact structure is often important in predicting the behavior of dynamical processes. We extend this notion to hypergraphs and motivate the importance of an analogous eigenvalue, the expansion eigenvalue, for hypergraph dynamical processes. Using a mean-field approach, we derive an approximation to the expansion eigenvalue in terms of the degree sequence for uncorrelated hypergraphs. We introduce a generative model for hypergraphs that includes degree assortativity, and use a perturbation approach to derive an approximation to the expansion eigenvalue for assortative hypergraphs. We define the dynamical assortativity, a dynamically sensible definition of assortativity for uniform hypergraphs, and describe how reducing the dynamical assortativity of hypergraphs through preferential rewiring can extinguish epidemics. We validate our results with both synthetic and empirical datasets.

Herd immunity and epidemic size in networks with vaccination homophily

We study how the herd immunity threshold and the expected epidemic size depend on homophily with respect to vaccine adoption. We find that the presence of homophily considerably increases the critical vaccine coverage needed for herd immunity and that strong homophily can push the threshold entirely out of reach. The epidemic size monotonically increases as a function of homophily strength for a perfect vaccine, while it is maximized at a nontrivial level of homophily when the vaccine efficacy is limited. Our results highlight the importance of vaccination homophily in epidemic modeling.

Systematic assessment of the quality of fit of the stochastic block model for empirical networks

We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive model checking as a criterion to assess the quality of fit, which involves comparing networks generated by the inferred model with the empirical network, according to a set of network descriptors. We observe that the SBM is capable of providing an accurate description for the majority of networks considered, but falls short of saturating all modeling requirements. In particular, networks possessing a large diameter and slow-mixing random walks tend to be badly described by the SBM. However, contrary to what is often assumed, networks with a high abundance of triangles can be well described by the SBM in many cases. We demonstrate that simple network descriptors can be used to evaluate whether or not the SBM can provide a sufficiently accurate representation, potentially pointing to possible model extensions that can systematically improve the expressiveness of this class of models.

Spatially mapping the immune landscape of melanoma using imaging mass cytometry

Melanoma is an immunogenic cancer with a high response rate to immune checkpoint inhibitors (ICIs). It harbors a high mutation burden compared with other cancers and, as a result, has abundant tumor-infiltrating lymphocytes (TILs) within its microenvironment. However, understanding the complex interplay between the stroma, tumor cells, and distinct TIL subsets remains a substantial challenge in immune oncology. To properly study this interplay, quantifying spatial relationships of multiple cell types within the tumor microenvironment is crucial. To address this, we used cytometry time-of-flight (CyTOF) imaging mass cytometry (IMC) to simultaneously quantify the expression of 35 protein markers, characterizing the microenvironment of 5 benign nevi and 67 melanomas. We profiled more than 220,000 individual cells to identify melanoma, lymphocyte subsets, macrophage/monocyte, and stromal cell populations, allowing for in-depth spatial quantification of the melanoma microenvironment. We found that within pretreatment melanomas, the abundance of proliferating antigen-experienced cytotoxic T cells (CD8⁺CD45RO⁺Ki67⁺) and the proximity of antigen-experienced cytotoxic T cells to melanoma cells were associated with positive response to ICIs. Our study highlights the potential of multiplexed single-cell technology to quantify spatial cell-cell interactions within the tumor microenvironment to understand immune therapy responses.

A general framework to link theory and empirics in opinion formation models

We introduce a minimal opinion formation model that is quite flexible and can reproduce a wide variety of the existing micro-influence assumptions and models. The model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically via Monte Carlo simulations. Our analysis is exemplified by recently reported empirical data drawn from an online social network. We demonstrate that the model calibrated on these data may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms.

Sexual mixing patterns among male-female partnerships in Melbourne, Australia

BACKGROUND

Individuals who have both opposite- and same-sex partners have the potential to pass sexually transmitted infections (STIs) between high- and low-risk populations. Our aim was to examine assortative sexual mixing in terms of same-sex activity among male-female partnerships.

METHODS

This was a retrospective repeated cross-sectional study of male-female partnerships attending the Melbourne Sexual Health Centre (MSHC) from 2015 to 2019. Sex of sexual partners was collected via computer-assisted self-interview. We calculated the proportion of partnerships where at least one individual reported same-sex partners in the previous 12months and the degree of assortativity by bisexuality.

RESULTS

A total of 2112 male-female partnerships (i.e. 4224 individuals) were included, with a median age of 27 years (IQR 23-31). Overall, 89.3% (1885/2112) of male-female partnerships did not report any other same-sex partners; however, in 9.5% (201/2112) of partnerships, same-sex partners were reported by one individual and in 1.2% (26/2112) of partnerships, both individuals reported same-sex partners. Bisexuality appeared to be slightly assortative in male-female partnerships (r=0.163, 95% CI: 0.150-0.176; P<0.001).

CONCLUSION

One in 10 individuals in male-female partnerships had at least one same-sex partner within the previous 12months. Individuals were minorly selective by bisexuality, suggesting the patterns of bisexual mixing in male-female partners are more variable and this may have a significant impact on STI transmission in heterosexual populations.

Asymmetric Relatedness from Partial Correlation

Relatedness is a key concept in economic complexity, since the assessment of the similarity between industrial sectors enables policymakers to design optimal development strategies. However, among the different ways to quantify relatedness, a measure that takes explicitly into account the time correlation structure of exports is still lacking. In this paper, we introduce an asymmetric definition of relatedness by using statistically significant partial correlations between the exports of economic sectors and we apply it to a recently introduced database that integrates the export of physical goods with the export of services. Our asymmetric relatedness is obtained by generalising a recently introduced correlation-filtering algorithm, the partial correlation planar graph, in order to allow its application on multi-sample and multi-variate datasets, and in particular, bipartite temporal networks. The result is a network of economic activities whose links represent the respective influence in terms of temporal correlations; we also compute the statistical confidence of the edges in the network via an adapted bootstrapping procedure. We find that the underlying influence structure of the system leads to the formation of intuitively-related clusters of economic sectors in the network, and to a relatively strong assortative mixing of sectors according to their complexity. Moreover, hub nodes tend to form more robust connections than those in the periphery.