Structural Analysis With Graphs

2020 ◽  
pp. 31-47
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Structural Analysis ◽  
Network Analysis ◽  
Social Research ◽  
Analysis Methods

Graphs are mathematical formalisms that represent social networks very well. Analysis methods using graph theory have started to develop substantially along with the advancement of mathematics and computer sciences in recent years, with contributions from several disciplines including social network analysis. Learning how to use graphs to represent social networks is important not only for employing theoretical insights of this advanced field in social research, but also for the practical purposes of utilizing its mature and abundant tools. This chapter explores structural analysis with graphs.

scholarly journals Group Measures and Modeling for Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Vincent Levorato
Keyword(s):  
Complex System ◽  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Modeling ◽  
Complex Interactions ◽  
Social Network Modeling

Social network modeling is generally based on graph theory, which allows for study of dynamics and emerging phenomena. However, in terms of neighborhood, the graphs are not necessarily adapted to represent complex interactions, and the neighborhood of a group of vertices can be inferred from the neighborhoods of each vertex composing that group. In our study, we consider that a group has to be considered as a complex system where emerging phenomena can appear. In this paper, a formalism is proposed to resolve this problematic by modeling groups in social networks using pretopology as a generalization of the graph theory. After giving some definitions and examples of modeling, we show how some measures used in social network analysis (degree, betweenness, and closeness) can be also generalized to consider a group as a whole entity.

scholarly journals Network Analysis of Political Internet Communities: from Formalized to «Unobserved» Groups

2021 ◽  
pp. 160-182
Author(s):  
Olga Popova ◽  
Sergey Suslov
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Communities ◽  
Latent Dirichlet Allocation ◽  
The Political ◽  
Analysis Methods ◽  
Methods And Techniques ◽  
Political Communities

The article is dedicated to the development of the political communities in social networks analysis methods. Main stages of network approach in the political science is described in the research. Researchers review the most significant methods and techniques in the political online communities studies for the last decade. The article shows the contemporary Russian scientists contribution in the development of online communities learning techniques. Networks and social network analysis methods and techniques become universal scientific approaches for several scientific fields. Boundary-transcending trends were critical means of science integration. Researchers present the results of experiment in which evaluate the possibilities of study unobserved political groups using latent Dirichlet allocation (LDA) model. The brief LDA foundation history and possible modifications for social topic modeling based on social networks data are discribed in the review. Using sample from one feed aggregator telegram channel in period of 2020 autumn, the authors display the most valuable topics in the Russian segment of political communication. Also it provides communities ideological preferences. Modified qualitative sociological methods can be used in online political communities discursive features research without any specific computer science techniques. Since about 70% of the Internet data are generated in the social networks, velocity and volume data necessitate new data mining techniques, databases capacity and computation processes. In other words, it provides a big data approach in social network analysis.

scholarly journals Data Mining in Social Networks and its Application in Counterterrorism

2019 ◽  
Vol 8 (3) ◽  
pp. 1278-1284
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Theory Analysis ◽  
Graph Theory Analysis ◽  
Insight Into ◽  
Novel Algorithm

Social Networks are best represented as complex interconnected graphs. Graph theory analysis can hence be used for insight into various aspects of these complex social networks. Privacy of such networks lately has been challenged and a detailed analysis of such networks is required. This paper applies key graph theory concepts to analyze such social networks. Moreover, it also discusses applications and proposal of a novel algorithm to analyze and gather key information from terrorist social networks. Investigative Data Mining is used for this which is defined as when Social Network Analysis (SNA) is applied to Terrorist Networks to gather useful insights about the network..

scholarly journals Clique Comparison and Homophily Detection in Telecom Social Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 81-87
Author(s):  
Marin Mandić ◽  
Davor Škobić ◽  
Goran Martinović
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Phone Call ◽  
Network Data ◽  
Social Connectivity ◽  
Telecom Industry ◽  
Virtual Operator

Social Network Analysis (SNA) is based on graph theory and is used for identification of the structure, behavioral patterns and social connectivity of entities. In this paper, SNA is used in the telecom industry in terms of a call detail record referring to phone call data separated into two groups, i.e., domicile network and virtual operator network data. Emphasis was placed on community detection. Comparison was made among communities detected in domicile and virtual operator networks. Results show that in contrast to domicile network, the number of cliques in the virtual operator network is larger. Also, homophily was detected between domicile network and virtual operator network users.

Use of Social Network Analysis in Telecommunication Domain

2018 ◽  
pp. 152-178 ◽  
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Hrudaya Kumar Tripathy ◽  
Monalisa Mishra ◽  
Bijayalaxmi Panda
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Other ◽  
Centrality Measures ◽  
Customer Data ◽  
Relationship Types ◽  
Heterogeneous Social Networks

Social network analysis (SNA) is the analysis of social communication through network and graph theory. In our chapter the application of SNA has been explored in telecommunication domain. Telecom data consist of Customer data and Call Detail Data (CDR). The proposed work, considers the attributes of call detail data and customer data as different relationship types to model our Multi-relational Telecommunication social network. Typical work on social network analysis includes the discovery of group of customers who shares similar properties. A new challenge is the mining of hidden communities on such heterogeneous social networks, to group the customers as churners and non-churners in Telecommunication social network. After the analysis of the available data we constructed a Weights Multi-relational Social Network, in which each relation carry a different weight, representing how close two customers are with one another. The centrality measures depict the intensity of the customer closeness, hence we can determine the customer who influence the other customer to churn.

scholarly journals Social Network Analysis: Towards Complexity Problem

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 449-461
Author(s):  
Mahyuddin K.M. Nasution ◽  
Rahmad Syah ◽  
Marischa Elveny
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Models ◽  
Social Actors ◽  
The Social ◽  
Complexity Problem ◽  
Computational Procedures

Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.

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.

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