Exponential random graph model parameter estimation for very large directed networks

Democratic governance and global science: A longitudinal analysis of the international research collaboration network

The democracy-science relationship has traditionally been examined through philosophical conjecture and country case studies. There remains limited global-scale empirical research on the topic. This study explores country-level factors related to the dynamics of the global research collaboration network, focusing on structural associations between democratic governance and the strength of international research collaboration ties. This study combines longitudinal data on 170 countries between 2008 and 2017 from the Varieties of Democracy Institute, World Bank Indicators, Scopus, and Web of Science bibliometric data. Methods include descriptive network analysis, temporal exponential random graph models (TERGM), and valued exponential random graph models (VERGM). The results suggest significant positive effects of democratic governance on the formation and strength of international research collaboration ties and homophily between countries with similar levels of democratic governance. The results also show the importance of exogenous factors, such as GDP, population size, and geographical distance, as well as endogenous network factors, including preferential attachment and transitivity.

Maximum entropy networks for large scale social network node analysis

Recently proposed computational techniques allow the application of various maximum entropy network models at a larger scale. We focus on disinformation campaigns and apply different maximum entropy network models on the collection of datasets from the Twitter information operations report. For each dataset, we obtain additional Twitter data required to build an interaction network. We consider different interaction networks which we compare to an appropriate null model. The null model is used to identify statistically significant interactions. We validate our method and evaluate to what extent it is suited to identify communities of members of a disinformation campaign in a non-supervised way. We find that this method is suitable for larger social networks and allows to identify statistically significant interactions between users. Extracting the statistically significant interaction leads to the prevalence of users involved in a disinformation campaign being higher. We found that the use of different network models can provide different perceptions of the data and can lead to the identification of different meaningful patterns. We also test the robustness of the methods to illustrate the impact of missing data. Here we observe that sampling the correct data is of great importance to reconstruct an entire disinformation operation.

Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration

Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.

Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics

The value of digital twins for prototyping controllers or interventions in a sandbox environment are well-established in engineering and physics. However, this is challenging for biophysics trying to seamlessly compose models of multiple spatial and temporal scale behavior into the digital twin. Two challenges stand out as constraining progress: (i) ensuring physical consistency of conservation laws across composite models and (ii) drawing useful and timely clinical and scientific information from conceptually and computationally complex models. Challenge (i) can be robustly addressed with bondgraphs. However, challenge (ii) is exacerbated using this approach. The complexity question can be looked at from multiple angles. First from the perspective of discretizations that reflect underlying biophysics (functional tissue units) and secondly by exploring maximum entropy as the principle guiding multicellular biophysics. Statistical mechanics, long applied to understanding emergent phenomena from atomic physics, coupled with the observation that cellular architecture in tissue is orchestrated by biophysical constraints on metabolism and communication, shows conceptual promise. This architecture along with cell specific properties can be used to define tissue specific network motifs associated with energetic contributions. Complexity can be addressed based on energy considerations and finding mean measures of dependent variables. A probability distribution of the tissue's network motif can be approximated with exponential random graph models. A prototype problem shows how these approaches could be implemented in practice and the type of information that could be extracted.

Comparing the Real-World Performance of Exponential-family Random Graph Models and Latent Order Logistic Models for Social Network Analysis

Exponential-family Random Graph models (ERGM) are widely used in social network analysis when modelling data on the relations between actors. ERGMs are typically interpreted as a snapshot of a network at a given point in time or in a final state. The recently proposed Latent Order Logistic model (LOLOG) directly allows for a latent network formation process. We assess the real-world performance of these models when applied to typical networks modelled by researchers. Specifically, we model data from an ensemble of articles in the journal Social Networks with published ERGM fits, and compare the ERGM fit to a comparable LOLOG fit. We demonstrate that the LOLOG models are, in general, in qualitative agreement with the ERGM models, and provide at least as good a model fit. In addition they are typically faster and easier to fit to data, without the tendency for degeneracy that plagues ERGMs. Our results support the general use of LOLOG models in circumstances where ERGMs are considered.

Multiple clocks in network evolution

Relational event models shift the analytical focus away from network ties defined in terms of transitions between mutually exclusive states of connectivity, to bonding processes emerging from observable flows linking senders and receivers of action. In this framework, the possibility to connect social mechanisms of theoretical interest to sequences of observed relational events depends on the relative speed at which these mechanisms operate. Building on established non-parametric methods in survival analysis, in this paper we introduce a new approach to the analysis of the internal time distribution of relational mechanisms of broad theoretical interest in research on the evolutionary dynamics of social and other kinds of networks. We propose general algorithms that may be adopted to study the time structure of theoretically relevant network mechanisms. We illustrate the practical value of our proposal in an analysis of a large sample of high-frequency financial transactions observed over a period of 11 years. We show how the internal time structure of the social mechanisms that control flows of market transactions is sensitive to institutional change in transaction regimes induced by successive financial crises. The results we report invite reflection on a new notion of network “structure” incorporating change as one of its constitutive elements. The study suggests a number of conjectures that provide broad conceptual bases for the development of testable hypotheses about the forces that shape the evolutionary dynamics of network structure.

Testing biological network motif significance with exponential random graph models

Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-021-00434-y.

Socio-spatial relations observed in the global city network of firms

One of the prevailing approaches to the study of the global economy is the analysis of global city networks based on the activities of multinational firms. Research in this vein generally conceptualises cities as nodes, and the intra-firm relations between them as ties, forming the building blocks for globally scaled interurban networks. While such an approach has provided a valuable heuristic for understanding how cities are globally connected, and how the global economy can be conceived of as a network of cities, there is a lack of understanding as to how and why cities are connected, and which factors contribute to the existence of ties between cities. Here, we explain how five distinct socio-spatial dimensions contribute to global city network structure through their diverse effects on interurban dyads. Based on data from 13,583 multinational firms with 163,821 international subsidiary locations drawn from 208 global securities exchanges, we hypothesise how regional, linguistic, industrial, developmental, and command & control relations may contribute to network structure. We then test these by applying an exponential random graph model (ERGM) to explain how each dimension may contribute to cities' embeddedness within the overall network. Though all are shown to shape interurban relations to some extent, we find that two cities sharing a common industrial base are more likely to be connected. The ERGM also reveals a strong core-periphery structure in that cities in middle- and low-income countries are more reliant on connectivity than those in high-income countries. Our findings indicate that, despite claims seeking to de-emphasise the top-heavy organisational structure of the global urban economic network, interurban relations are characterised by uneven global development in which socio-spatial embeddedness manifests through a combination of similarity (homophily) and difference (heterophily) as determined by heterogeneous power relationships underlying global systems of production, exchange and consumption.

New Network Models for the Analysis of Social Contagion in Organizations: An Introduction to Autologistic Actor Attribute Models

Autologistic actor attribute models (ALAAMs) provide new analytical opportunities to advance research on how individual attitudes, cognitions, behaviors, and outcomes diffuse through networks of social relations in which individuals in organizations are embedded. ALAAMs add to available statistical models of social contagion the possibility of formulating and testing competing hypotheses about the specific mechanisms that shape patterns of adoption/diffusion. The main objective of this article is to provide an introduction and a guide to the specification, estimation, interpretation and evaluation of ALAAMs. Using original data, we demonstrate the value of ALAAMs in an analysis of academic performance and social networks in a class of graduate management students. We find evidence that both high and low performance are contagious, that is, diffuse through social contact. However, the contagion mechanisms that contribute to the diffusion of high performance and low performance differ subtly and systematically. Our results help us identify new questions that ALAAMs allow us to ask, new answers they may be able to provide, and the constraints that need to be relaxed to facilitate their more general adoption in organizational research.