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.

scholarly journals Edge overlap in weighted and directed social networks

2021 ◽  
pp. 1-15
Author(s):  
Heather Mattie ◽  
Jukka-Pekka Onnela
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Network Data ◽  
Directed Networks ◽  
Rural Villages ◽  
Social Network Data ◽  
Structure Strength ◽  
Mean And Variance ◽  
The Mean

Abstract With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here, we extend definitions of edge overlap to weighted and directed networks and present closed-form expressions for the mean and variance of each version for the Erdős–Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks, and our derivations provide a statistically rigorous way for comparing edge overlap across networks.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

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 Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

Network Basics: Points, Lines, and Positions

2020 ◽  
pp. 15-33
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Ethical Issues ◽  
Building Blocks ◽  
Levels Of Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Basic Concepts ◽  
Statistical Approaches

This chapter presents an introduction to the basic concepts central to social network analysis. Written for those with little experience in the approach, the chapter aims to provide the necessary tools to dig deeper into exploring social networks via the subsequent chapters in this volume. It begins by introducing the building blocks of networks—nodes and edges—and their characteristics. Next, it outlines several of the major dimensions of network analysis, including the implications of boundary specification and levels of analysis. It also briefly introduces statistical approaches to networks and network data collection. The chapter concludes with a discussion of ethical issues that arise when collecting and analyzing social network data.

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.

scholarly journals An efficient method to detect communities in social networks

2021 ◽  
Author(s):  
MEHJABIN KHATOON ◽  
W AISHA BANU
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Average Degree ◽  
Data Sets ◽  
Time Data ◽  
Network Graph ◽  
The Social ◽  
Metric Functions

Abstract Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with four community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, Leiden algorithm and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.

7. Very Local Structure in Social Networks

2007 ◽  
Vol 37 (1) ◽  
pp. 209-256 ◽  
Author(s):  
Katherine Faust
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Social Relations ◽  
Lower Order ◽  
Local Network ◽  
Social Structural ◽  
Network Properties ◽  
Structural Configurations ◽  
Low Dimensional

Triadic configurations are fundamental to many social structural processes and provide the basis for a variety of social network theories and methodologies. This paper addresses the question of how much of the patterning of triads is accounted for by lower-order properties pertaining to nodes and dyads. The empirical base is a collection of 82 social networks representing a number of different species (humans, baboons, macaques, bison, cattle, goats, sparrows, caribou, and more) and an assortment of social relations (friendship, negative sentiments, choice of work partners, advice seeking, reported social interactions, victories in agonistic encounters, dominance, and co-observation). Methodology uses low dimensional representations of triad censuses for these social networks, as compared to censuses expected given four lower-order social network properties. Results show that triadic structure is largely accounted for by properties more local than triads: network density, nodal indegree and outdegree distributions, and the dyad census. These findings reinforce the observation that structural configurations that can be realized in empirical social networks are severely constrained by very local network properties, making some configurations extremely improbable.

Networks of Co-Authorship

2012 ◽  
pp. 981-994
Author(s):  
Antonio José Caulliraux Pithon ◽  
Ralfh Varges Ansuattigui ◽  
Paulo Enrique Stecklow
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Morphological Characteristics ◽  
Network Structures ◽  
Communication Processes ◽  
The Social ◽  
The Subject

The networks are transorganizational arrangements forming a structure and, in a more abstract and generic manner, are built from the interactions between individuals and organizations. These interactions allow the emergence of network structures more related to personal ties and the types of existing social interactions between the actors. Social networks aren’t a recent enterprise, but have been the subject of deeper studies due to universalization and convergence of communication processes, fundamental to the establishment and proliferation of networks. The structure where networks are manifested calls for horizontality, where there is no formal hierarchy of the elements that comprise it, composed by nodes elements and lines elements. This article analyzes the social network of authorship of one of five Postgraduate Programs of CEFET/RJ, presenting the connections between network teachers, justifying the morphological characteristics of the network and suggesting methodologies for continuing the study for the teaching and researching networks.

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