scholarly journals The Mathematics of Social Network Analysis: Metrics for Academic Social Networks

2015 ◽  
Vol 4 (12) ◽  
pp. 889-893 ◽  
Author(s):  
Tasleem Arif
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Academic Social Networks ◽  
Network Analysis Metrics

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 Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

Predicting Violent Victimization Using Social Network Analysis From Police Data

2021 ◽  
Vol 36 (3) ◽  
pp. 436-454
Author(s):  
Andrew M. Fox ◽  
Kenneth J. Novak ◽  
Tinneke Van Camp ◽  
Chadley James
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Violent Crime ◽  
Violent Victimization ◽  
Crime Victimization ◽  
Effective Prevention ◽  
Police Data ◽  
Assault Victims

Extant research suggests that membership in crime networks explains vulnerability to violent crime victimization. Consequently, identifying deviant social networks and understanding their structure and individual members' role in them could provide insight into victimization risk. Identifying social networks may help tailor crime prevention strategies to mitigate victimization risks and dismantle deviant networks. Social network analysis (SNA) offers a particular means of comprehending and measuring such group-level structures and the roles that individuals play within them. When applied to research on crime and victimization, it could provide a foundation for developing precise, effective prevention, intervention, and suppression strategies. This study uses police data to examine whether individuals most central to a deviant social network are those who are most likely to become victims of violent crime, and which crime network roles are most likely to be associated with vulnerability to violent victimization. SNA of these data indicates that network individuals who are in a position to manage the flow of information in the network (betweenness centrality), regardless of their number of connections (degree centrality), are significantly more likely to be homicide and aggravated assault victims. Implications for police practice are discussed.

Entanglement, Materiality and the Social Organisation of Construction Workers in Classical Athens

2018 ◽  
pp. 512-528
Author(s):  
Diane Harris Cline
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Theory ◽  
Actor Network Theory ◽  
Fifth Century ◽  
Actor Network ◽  
The Social ◽  
Building Program

This chapter views the “Periclean Building Program” through the lens of Actor Network Theory, in order to explore the ways in which the construction of these buildings transformed Athenian society and politics in the fifth century BC. It begins by applying some Actor Network Theory concepts to the process that was involved in getting approval for the building program as described by Thucydides and Plutarch in his Life of Pericles. Actor Network Theory blends entanglement (human-material thing interdependence) with network thinking, so it allows us to reframe our views to include social networks when we think about the political debate and social tensions in Athens that arose from Pericles’s proposal to construct the Parthenon and Propylaea on the Athenian Acropolis, the Telesterion at Eleusis, the Odeon at the base of the South slope of the Acropolis, and the long wall to Peiraeus. Social Network Analysis can model the social networks, and the clusters within them, that existed in mid-fifth century Athens. By using Social Network Analysis we can then show how the construction work itself transformed a fractious city into a harmonious one through sustained, collective efforts that engaged large numbers of lower class citizens, all responding to each other’s needs in a chaine operatoire..

Local Community Extraction in Social Network Analysis

2013 ◽  
pp. 172-181
Author(s):  
Xianchao Zhang ◽  
Liang Wang ◽  
Yueting Li ◽  
Wenxin Liang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Local Community ◽  
Linear Time ◽  
Complete Information ◽  
Global Community ◽  
Local Algorithms ◽  
Community Extraction

To identify global community structures in networks is a great challenge that requires complete information of graphs, which is infeasible for some large networks, e.g. large social networks. Recently, local algorithms have been proposed to extract communities for social networks in nearly linear time, which only require a small part of the graphs. In local community extraction, the community extracting assignments are only done for a certain subset of vertices, i.e., identifying one community at a time. Typically, local community detecting techniques randomly start from a vertex and gradually merge neighboring vertices one-at-a-time by optimizing a measure metric. In this chapter, plenty of popular methods are presented that are designed to obtain a local community for a given graph.

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

2018 ◽  
pp. 173-188
Author(s):  
Michele A. Brandão ◽  
Matheus A. Diniz ◽  
Guilherme A. de Sousa ◽  
Mirella M. Moro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Online Tool ◽  
Strength Of Ties ◽  
E Learning

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

Web Mining and Social Network Analysis

Data Mining ◽  
2013 ◽  
pp. 326-335
Author(s):  
Roberto Marmo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Web Mining ◽  
Social Network Mining ◽  
Data Mining Algorithms ◽  
Instant Messenger ◽  
Mining Algorithms

Research on social networks has advanced significantly due to wide variety of on-line social websites and very popular Web 2.0 application. Social network analysis views social relationships in terms of network and graph theory about nodes (individual actors within the network) and ties (relationships between the actors). Using web mining techniques and social networks analysis it is possible to process and analyze large amount of social data (such as blogtagging, online game playing, instant messenger etc.) and by this to discover valuable information from data. In this way, we can understand the social structure, social relationships and social behaviors. This new approach is also denoted as social network mining. These algorithms differ from established set of data mining algorithms developed to analyze individual records, because social network datasets are called relational due to centrality of relations among entities. This chapter also sets out a process to apply web mining.

Reciprocation of Indian States on Trade Relation

2021 ◽  
pp. 249-261
Author(s):  
Rabindranath Jana ◽  
P. Vdhyarani ◽  
R. Maruthakutti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Trade Flows ◽  
Market Place ◽  
Trade Relation ◽  
Initial Attempt ◽  
Indian States ◽  
Internal Integration

In the past few years, it is being observed that there is a wake-up call for creating one economic India, one market place with free movement of goods and people. Again, for creating one economic India, the needs of creating one economic India, it needs to preserve sovereignty for the Indian states. However, it is very pertinent to ask how much internal integration India has achieved through trade between states within India. Under such brief backdrop, the author has tried, as an initial attempt, to apply social network analysis (SNA) for studying empirically reciprocation/cohesiveness of Indian states using the data on inter-firm and intra-firm trade flows between states for the financial year 2015-2016. On the basis of reciprocity counts for weighted social networks on inter-states trade relation, the standardized reciprocity measures have been adopted for the chapter. The outcomes of the chapter seem to offer important implications for understanding cooperation and integration on inter-states trade interactions and to exhibit the equilibrium and circularity of inter-state trade flows.

Case Study

2021 ◽  
pp. 345-366
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
User Research ◽  
Social Games ◽  
Work Done

This chapter discusses Social Network Analysis, a technique used to analyze social networks within social games as a method to enhance retention in games. We will show how one can use this method by applying it to the problem of retention within the game Tom Clancy’s The Division (TCTD). Using the game and the analysis will help you understand how to use SNA to understand types of players and influential players, and, as a result, understand how to engage different players, especially influencers, to increase retention. While the chapter will focus on the use of SNA for TCTD as a case study, the methods discussed under SNA can be applied to other types of games. Please note that this chapter is an extension of the work done by several collaborators to the authors, including Casper Harteveld (professor, Northeastern University), Sebastian Deterding (professor, York University), and Ahmad Azadvar (User Research Lead at Ubisoft Massive), and the work was accomplished with the support of Ubisoft, the Games Lab, and the Live Ops team at Massive Entertainment.

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