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Graph theoretic methods in multiagent networks
This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems.
The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications.
The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications.
This book has been adopted as a textbook at the following universities:
University of Stuttgart, GermanyRoyal Institute of Technology, SwedenJohannes Kepler University, AustriaGeorgia Tech, USAUniversity of Washington, USAOhio University, USA
eBook
The Study of Psychopathology from the Network Analysis Perspective
Background: Network analysis (NA) is an analytical tool that allows one to explore the map of connections and eventual dynamic influences among symptoms and other elements of mental disorders. In recent years, the use of NA in psychopathology has rapidly grown, which calls for a systematic and critical analysis of its clinical utility. Methods: Following PRISMA guidelines, a systematic review of published empirical studies applying NA in psychopathology, between 2010 and 2017, was conducted. We included the literature published in PubMed and PsycINFO using as keywords any combination of “network analysis” with the terms “anxiety,” “affective disorders,” “depression,” “schizophrenia,” “psychosis,” “personality disorders,” “substance abuse” and “psychopathology.” Results: The review showed that NA has been applied in a plethora of mental disorders in adults (i.e., 13 studies on anxiety disorders; 19 on mood disorders; 7 on psychosis; 1 on substance abuse; 1 on borderline personality disorder; 18 on the association of symptoms between disorders), and 6 on childhood and adolescence. Conclusions: A critical examination of the results of each study suggests that NA helps to identify, in an innovative way, important aspects of psychopathology like the centrality of the symptoms in a given disorder as well as the mutual dynamics among symptoms. Yet, despite these promising results, the clinical utility of NA is still uncertain as there are important limitations on the analytic procedures (e.g., reliability of indices), the type of data included (e.g., typically restricted to secondary analysis of already published data), and ultimately, the psychometric and clinical validity of the results.
Journal Article
The Role of Space in the Formation of Social Ties
Recent years have seen a resurgence of interest in the relation between networks and spatial context. This review examines critically a selection of the literature on how physical space affects the formation of social ties. Different aspects of this question have been a feature in network analysis, neighborhood research, geography, organizational science, architecture and design, and urban planning. Focusing primarily on work at the meso- and microlevels of analysis, we pay special attention to studies examining spatial processes in neighborhood and organizational contexts. We argue that spatial context plays a role in the formation of social ties through at least three mechanisms, spatial propinquity, spatial composition, and spatial configuration; that fully capturing the role of spatial context will require multiple disciplinary perspectives and both qualitative and quantitative research; and that both methodological and conceptual questions central to the role of space in networks remain to be answered. We conclude by identifying major challenges in this work and proposing areas for future research.
Journal Article
Students' Social-Cognitive Engagement in Online Discussions: An Integrated Analysis Perspective
Grounded on constructivism, mining a complex mix of social and cognitive interrelations is key to understanding collaborative discussion in online learning. A single examination of one of these factors tends to overlook the impact of the other factor on learning. In this paper, we innovatively constructed a social-cognitive engagement setting to jointly characterize social and cognitive aspects. In the online discussion forum, this study jointly characterized students' social and cognitive aspects to investigate interactive patterns of different social-cognitive engagements and social-cognitive engagement evolution across four periods (i.e., creation, growth, maturity, and death). Multi-methods including social network analysis, content analysis, epistemic network analysis, and statistical analysis was applied in this study. The results showed that the interactive patterns of social-cognitive engagement were affected by both social network position and cognitive level. In particular, students' social network position was a vital indicator for the contributions to cognitive level of students, and cognitive level affected the related interactions to some extent. In addition, this study found a nonlinear evolutionary development of students' social-cognitive engagement. Furthermore, maturity is a critical period on which teachers should focus, as the co-occurrence of social-cognitive engagement reaches a maximum level in this period. Based on the results, this multi-perspective analysis including social and cognitive aspects can provide insightful methodological implications and practical suggestions for teachers in conducting in-depth interactive discussions.
Journal Article
Common permutation methods in animal social network analysis do not control for non-independence
The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutations. We show that, contrary to accepted wisdom, node-label permutations do not automatically account for the non-independences assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same assumption also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of non-independence, parametric regression models can also produce accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we suggest that standard parametric models could be used in the place of permutation-based methods. Moving away from permutation-based methods could have several benefits, including reducing over-reliance on p-values, generating more reliable effect size estimates, and facilitating the adoption of causal inference methods and alternative types of statistical analysis.
Journal Article