Social Networks in Information Systems

2010 ◽  
pp. 392-410 ◽  
Author(s):  
Hernâni Borges de Freitas ◽  
Alexandre Barão ◽  
Alberto Rodrigues da Silva
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Information Exchange ◽  
Group Processes ◽  
Communication Tools ◽  
Social Entities

A social network represents a set of social entities that interact through relationships like friendship, co-working, or information exchange. Social Network Analysis studies the patterns of relationships among social entities and can be used to understand and improve group processes. The arrival of new communication tools and networking platforms, especially the Web 2.0 Social Networking Services, opened new opportunities to explore the power of social networks inside and outside organizations. This chapter surveys the basic concepts of social networks methods, approaches, tools, and services. In particular, this chapter analyzes state-of-the-art social networks, explaining how useful Social Network Analysis can be in different contexts and how social networks can be represented, extracted, and analyzed in information systems.

Social Networks in Information Systems

2011 ◽  
pp. 127-144
Author(s):  
Hernâni Borges de Freitas ◽  
Alexandre Barão ◽  
Alberto Rodrigues da Silva
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Information Exchange ◽  
Group Processes ◽  
Communication Tools ◽  
Social Entities

A social network represents a set of social entities that interact through relationships like friendship, co-working, or information exchange. Social Network Analysis studies the patterns of relationships among social entities and can be used to understand and improve group processes. The arrival of new communication tools and networking platforms, especially the Web 2.0 Social Networking Services, opened new opportunities to explore the power of social networks inside and outside organizations. This chapter surveys the basic concepts of social networks methods, approaches, tools, and services. In particular, this chapter analyzes state-of-the-art social networks, explaining how useful Social Network Analysis can be in different contexts and how social networks can be represented, extracted, and analyzed in information systems.

Social Networks in Information Systems

2010 ◽  
pp. 169-187
Author(s):  
Hernâni Borges de Freitas ◽  
Alexandre Barão ◽  
Alberto Rodrigues da Silva
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Information Exchange ◽  
Group Processes ◽  
Communication Tools ◽  
Social Entities

A social network represents a set of social entities that interact through relationships like friendship, co-working, or information exchange. Social Network Analysis studies the patterns of relationships among social entities and can be used to understand and improve group processes. The arrival of new communication tools and networking platforms, especially the Web 2.0 Social Networking Services, opened new opportunities to explore the power of social networks inside and outside organizations. This chapter surveys the basic concepts of social networks methods, approaches, tools, and services. In particular, this chapter analyzes state-of-the-art social networks, explaining how useful Social Network Analysis can be in different contexts and how social networks can be represented, extracted, and analyzed in information systems.

Applications of Data Mining in Dynamic Social Network Analysis

2013 ◽  
pp. 110-121
Author(s):  
Manish Kumar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Knowledge Discovery ◽  
Online Social Networking ◽  
Dynamic Social Network ◽  
Business Goals

Social Networks are nodes consisting of people, groups and organizations growing dynamically. The growth is horizontal as well as vertical in terms of size and number. Social network analysis has gained success due to online social networking and sharing sites. The accessibility of online social sites such as MySpace, Facebook, Twitter, Hi5, Friendster, SkyRock and Beb offer sharing and maintaining large amount of different data. Social network analysis is focused on mining such data i.e. generating pattern of people’s interaction. The analysis involves the knowledge discovery that helps the sites as well as users in terms of usage and business goals respectively. Further it is desired that the process must be privacy preserving. This chapter describes the various mining techniques applicable on social networks data.

Detecting Communities in Dynamic Social Networks using Modularity Ensembles SOM

2018 ◽  
Vol 5 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Raju Enugala ◽  
Lakshmi Rajamani ◽  
Sravanthi Kurapati ◽  
Mohammad Ali Kadampur ◽  
Y. Rama Devi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Dynamic Networks ◽  
Research Problem ◽  
Self Organizing Maps ◽  
Community Strength ◽  
Dynamic Social Networks ◽  
Social Entities

Social network analysis has gained much importance these days. Social network analysis is the process of recording various patterns of interactions between a set of social entities. An important phenomenon that draws the attention of analysis is the emergence of communities in these networks. The understanding and detection of communities in these networks is a challenging research problem. However, approaches to detect communities have largely focused on identifying communities in static social networks. But real-world social networks are not always static. In fact, many social networks in reality (such as Facebook, Bebo and Twitter) are dynamic networks that frequently change over time. In this paper, a framework is proposed for community detection in dynamic social networks, which explores self-organizing maps (SOM) for cluster selection and modularity measure for community strength identification. Experimental results on synthetic network datasets show the effectiveness of the proposed approach.

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.

scholarly journals Combining Stakeholder- and Social Network- Analysis to Improve Regional Nature Conservation: A Case Study from Osnabrück, Germany

2021 ◽  
Author(s):  
Felix Przesdzink ◽  
Laura Mae Herzog ◽  
Florian Fiebelkorn
Keyword(s):  
Nature Conservation ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Exchange ◽  
Theory And Practice ◽  
Knowledge Mapping ◽  
Land User ◽  
User Groups ◽  
Conservation Projects

AbstractMany nature conservation projects fail primarily not because of a lack of knowledge about upcoming threats or viable conservation concepts but rather because of the inability to transfer knowledge into the creation of effective measures. Therefore, an increase in information exchange and collaboration between theory- and practice-oriented conservation actors, as well as between conservation actors, land user groups, and authorities may enhance the effectiveness of conservation goals. By considering the interactions between conservation stakeholders as social networks, social network analysis (SNA) can help identify structural optimization potential in these networks. The present study combines SNA and stakeholder analysis (SA) to assess the interactions between 34 conservation stakeholders in the major city and district of Osnabrück in northwestern Germany and offers insights into cost/benefit optimizations of these stakeholder interactions. Data were acquired using a pile sort technique and guideline-based expert interviews. The SA, based on knowledge mapping and SWOT (strength, weaknesses, opportunities, and threats) analysis, identified individual stakeholder’s complementary properties, indicating which among them would most benefit from mutual information exchange and collaboration. The SNA revealed discrepancies in information exchange and collaboration between theory- and practice-focused stakeholders. Conflicts were found predominantly between conservation associations, authorities and land user groups. Ecological research, funding, land-use conflicts, and distribution of conservation knowledge were identified as fields with high potential for increased information exchange and collaboration. Interviews also showed that the stakeholders themselves see many opportunities for increased networking in the region. The results are discussed in relation to the existing literature on nature conservation networks and used to recommend optimization measures for the studied network. Finally, the conclusion reflects upon the developed approach’s implications and possibilities for conservation stakeholders and planners in general.

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.

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