scholarly journals Measurement of Social Networks for Innovation within Community Disaster Resilience

2019 ◽  
Vol 11 (7) ◽  
pp. 1943 ◽  
Author(s):  
Joanna Wilkin ◽  
Eloise Biggs ◽  
Andrew Tatem
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Field Research ◽  
Disaster Resilience ◽  
Policy Planning ◽  
Critical Methodology ◽  
Research And Policy ◽  
Community Disaster Resilience

Disaster risk reduction (DRR) research has long recognised that social networks are a vital source of support during and after a shock. However, the quantification of this social support, primarily through its recognition as social capital, has proven problematic as there is no singular method for its measurement, invalidating the credibility of studies that try to correlate its effects with community disaster resilience. Within the wider resilience field, research that specifically utilises social networks as the focus of analysis is evolving. This paper provides a critical synthesis of how this developing discourse is filtering into community disaster resilience, reviewing empirical case studies from the Global South within DRR that use social network analysis and connectivity measurement. Our analysis of these studies indicates that a robust methodology utilising social network analysis is emerging, which offers opportunity for research cross-comparability. Our review also finds that without this bottom-up mapping, the implementation of top-down preparedness policy and procedures are likely to fail, resulting in the advocation of social network analysis as a critical methodology in future resilience research and policy planning.

How to Study Online Networking

2022 ◽  
pp. 360-374
Author(s):  
Fabio Corbisiero
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Dissemination ◽  
Field Research ◽  
Social Phenomena ◽  
The Past ◽  
Network Research

Social media and social networks are pervasive in the daily use as well as in a number of applications. Social media and social networks are also intertwined, as the social medial platforms also offer the opportunity to develop and analyze social networks. Over the past two decades, there has been an explosion of interest in network research through social network analysis. Network research is “warm” today, with the number of articles on the topic of social media and social networks nearly tripling in the past decade. This interweaving has been a further breakthrough within field research yielding explanations for social phenomena in a wide variety of new ways. Social network analysis (SNA) has been recognized as a powerful tool for representing social network structures and information dissemination on the web. Here, the authors review the kinds of things that sociologists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field, with emphasis on SNA research methodology.

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.

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