scholarly journals Modeling Network Dynamics

2017 ◽  
Author(s):  
Christopher Steven Marcum ◽  
David R. Schaefer
Keyword(s):  
Stochastic Process ◽  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
Network Science ◽  
Dynamic Network ◽  
Process Theory ◽  
Network Data ◽  
Network Change ◽  
Human Actors

One of the great lessons from the last half century of research on social networks is that relationships are constantly in flux. While much social network analysis focuses on static relationships between actors, there is also a rich tradition of work extending back to foundational studies in network science focused on the notion that network change is an indelible aspect of social life for human and non-human actors alike (e.g., Bott, 1957; Heider, 1946; Newcomb 1961; Rapoport, 1949; Sampson, 1969). Today, social network researchers benefit from this history in that a host of methods to collect and analyze such dynamic network data have been developed. Among them, the methods based on stochastic process theory have given rise to a paradigm where inferences and predictions can be made on the mechanisms that drive changes in social structure.

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

2012 ◽  
Vol 367 (1599) ◽  
pp. 2108-2118 ◽  
Author(s):  
Louise Barrett ◽  
S. Peter Henzi ◽  
David Lusseau
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
The Other ◽  
Feedback Process ◽  
Cognitive Evolution ◽  
Systems Approaches ◽  
Entropy Measures ◽  
Free Ranging ◽  
Source Of Information

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.

Limitation of degree information for analyzing the interaction evolution in online social networks

2014 ◽  
Vol 25 (10) ◽  
pp. 1450056 ◽  
Author(s):  
Ke-Ke Shang ◽  
Wei-Sheng Yan ◽  
Xiao-Ke Xu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Services ◽  
Online Social Networks ◽  
Dynamic Network ◽  
Interaction Network ◽  
Strength Distribution ◽  
Customer Churn ◽  
Dynamic Social Network ◽  
Temporal Interactions

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

Two Informational Complexity Measures in Social Networks and Agent Communities

2011 ◽  
pp. 57-65
Author(s):  
António Jorge Filipe Fonseca
Keyword(s):  
Stochastic Process ◽  
Social Networks ◽  
Social Network ◽  
Structural Properties ◽  
Information Sources ◽  
Complexity Measures ◽  
Dynamic Relation ◽  
Dynamic Measures

Several informational complexity measures rely on the notion of stochastic process in order to extract hidden structural properties behind the apparent randomness of information sources. Following an equivalence approach between dynamic relation evolution within a social network and a generic stochastic process two dynamic measures of network complexity are proposed.

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

Network Perspectives on Crime

2017 ◽  
Author(s):  
Jason Gravel ◽  
George E. Tita
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Peer Influence ◽  
Criminal Behavior ◽  
Social Ties ◽  
Organizational Level ◽  
Network Data ◽  
Adult Health ◽  
The Individual

Though often not mentioned by name, the importance of social networks in explaining criminal behavior, delinquency, and patterns has long been recognized in the study of crime. Theories that explain criminal behavior at the individual level being learned through the impacts of peer influences presume that the transmission of ideas and influences flow among social ties (networks) that link individuals. Cultural theories of crime work in the same way. At the community level, delinquency and criminal behavior are born among members of a community or group that adhere to a particular cultural set of norms or beliefs. The concentration of crime in particular geographic areas results when there are insufficient ties among local residents to affect informal social control in the area. Impacted neighborhoods are often described as socially isolated, lacking social ties to institutions of power that provide the investment and services needed in a healthy community. The history of the formation and activities of street gangs is a clear example of how understanding the ties among individuals, and between groups of these individuals, matter in our understanding these phenomena. Comprehending social ties among gangs and gang members and employment of social network analysis (SNA) have become mainstays of local law enforcement efforts to address the issue of gang violence. Much of the early criminological work that implicated social networks but did not explicitly acknowledge a network by name, or did not employ SNA on formal network data, did so because collecting such data is difficult at best and sometimes impossible. Though criminology has been a “late adopter” of SNA, the field is making great strides in this area. The National Longitudinal Study of Adolescent to Adult Health (Add Health) research program has provided a rich set of network data to explore issues of peer influence. Researchers are using carefully collected social network data at the individual and organizational level to better understand the ability of communities to self-regulate delinquency and crime in an area. Arrest data and field identification stops are being used to generate large networks in an effort to understand how one’s position in a larger social structure might be related to an actor’s involvement in future offending or victimization. As the field of criminology continues to adopt a network perspective in the study of crime, it is important to understand the development of social networks within the field. Critically examining the strengths and weaknesses of network data, especially in terms of the process by which data are generated, can lead to better applications of network analysis in the future.

Social networks in arid Australia: a review of concepts and evidence

2008 ◽  
Vol 30 (1) ◽  
pp. 167 ◽  
Author(s):  
R. R. J. McAllister ◽  
B. Cheers ◽  
T. Darbas ◽  
J. Davies ◽  
C. Richards ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Natural Resources ◽  
Social Structure ◽  
West Coast ◽  
Arid Regions ◽  
Weak Links ◽  
Network Data ◽  
Semi Arid

Arid systems are markedly different from non-arid systems. This distinctiveness extends to arid-social networks, by which we mean social networks which are influenced by the suite of factors driving arid and semi-arid regions. Neither the process of how aridity interacts with social structure, nor what happens as a result of this interaction, is adequately understood. This paper postulates three relative characteristics which make arid-social networks distinct: that they are tightly bound, are hierarchical in structure and, hence, prone to power abuses, and contain a relatively higher proportion of weak links, making them reactive to crisis. These ideas were modified from workshop discussions during 2006. Although they are neither tested nor presented as strong beliefs, they are based on the anecdotal observations of arid-system scientists with many years of experience. This paper does not test the ideas, but rather examines them in the context of five arid-social network case studies with the aim of hypotheses building. Our cases are networks related to pastoralism, Aboriginal outstations, the ‘Far West Coast Aboriginal Enterprise Network’ and natural resources in both the Lake-Eyre basin and the Murray–Darling catchment. Our cases highlight that (1) social networks do not have clear boundaries, and that how participants perceive their network boundaries may differ from what network data imply, (2) although network structures are important determinants of system behaviour, the role of participants as individuals is still pivotal, (3) and while in certain arid cases weak links are engaged in crisis, the exact structure of all weak links in terms of how they place participants in relation to other communities is what matters.

scholarly journals A multi-species repository of social networks

2018 ◽  
Author(s):  
Pratha Sah ◽  
José David Méndez ◽  
Shweta Bansal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Evolutionary Biology ◽  
Quantitative Information ◽  
Network Data ◽  
Data Format ◽  
Invaluable Tool ◽  
Multiple Species ◽  
Taxonomic Groups

AbstractSocial network analysis is an invaluable tool to understand the patterns, evolution, and consequences of sociality. Comparative studies over the spectrum of sociality across taxonomic groups are particularly valuable. Such studies however require quantitative information on social interactions across multiple species which is not easily available. We introduce the Animal Social Network Repository (ASNR) as the first multi-taxonomic repository that collates more than 650 social networks from 47 species, including those of mammals, reptiles, fish, birds, and insects. The repository was created by consolidating social network datasets from the literature on wild and captive animals into a consistent and easy-to-use network data format. The repository is archived at https://bansallab.github.io/asnr/. ASNR has tremendous research potential, including testing hypotheses in the fields of animal ecology, social behavior, epidemiology and evolutionary biology.

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