An overview of Software Applications for Social Network Analysis

2013 ◽  
Vol 3 (3) ◽  
pp. 71-77 ◽  
Author(s):  
Ioana-Alexandra Apostolato
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Probabilistic Models ◽  
Software Applications ◽  
Software Packages ◽  
Node Removal ◽  
Analytical Tools ◽  
Analysis Of Social Networks ◽  
Text Mining Tool

Abstract There is a great variety of software tools that has been developed within the last 20 years, as to facilitate and support the qualitative and quantitative analysis of social networks. This paper gives a brief overview of some of the most popular software packages for social network analysis: Pajek, UCINET 6, NetDraw, Gephi, E-Net, KeyPlayer 1, StOCNET and Automap. Pajek has efficient algorithms for the analysis of large networks, while UCINET 6 includes multiple analytical tools highly efficient for exploring and measuring social network structures. NetDraw, nested in UCINET 6, and Gephi allow network visualization. E-Net and KeyPlayer 1 satisfy rather specific and well-oriented purposes: ego-network analysis and network key-player operations (node removal or utilization). StOCNET provides a platform for statistical methods focusing on probabilistic models, while Automap is a text mining tool for analyzing text relational data.

Brazilian Government’s Training Network for Digital Inclusion

2014 ◽  
pp. 618-643
Author(s):  
Silvana Rossy de Brito ◽  
Aleksandra do Socorro da Silva ◽  
Dalton Lopes Martins ◽  
Cláudio Alex Jorge da Rocha ◽  
João Crisóstomo Weyl Albuquerque Costa ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Training Program ◽  
Large Scale ◽  
Training Programs ◽  
Digital Inclusion ◽  
Training Process ◽  
Analysis Of Social Networks

This chapter summarizes several previous studies on the analysis of social networks and presents some challenges in monitoring and evaluating large-scale training programs that make use of social networks. The main objective is to understand the dynamics and identify how information is shared among the participating agents of the training program. In this regard, the authors present various algorithms that apply metrics to social network analysis to assess the evolution of networks throughout the training process, and specifically, to discuss the application of these metrics in the evaluation of large-scale training programs for digital inclusion.

Exploring Shipwreck Data

Roman Seas ◽  
2020 ◽  
pp. 110-153
Author(s):  
Justin Leidwanger
Keyword(s):  
Social Network ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Environmental Parameters ◽  
Geographic Information ◽  
Late Antique ◽  
Quantitative Analyses ◽  
Analytical Tools

This chapter applies the proposed methodology to the working dataset of 67 wrecks. Varied quantitative analyses serve to contextualize spatial and diachronic trends in the study area against the broad backdrop of the east and Mediterranean as a whole. Two discrete peaks of activity provide the basis for constructing comparative Social Network Analysis visualizations of Roman and Late Antique connections within and among the geographical areas represented by cargos. The analytical tools of Geographic Information Systems, together with environmental parameters and seafaring capabilities, allow these network links to be grounded spatially using likely sailing times; such journey lengths reflect the “costs”—and therefore the potential regularity and investment—represented by these connections.

scholarly journals Learning and robustness to catch-and-release fishing in a shark social network

2017 ◽  
Vol 13 (3) ◽  
pp. 20160824 ◽  
Author(s):  
Johann Mourier ◽  
Culum Brown ◽  
Serge Planes
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Cohesion ◽  
Network Robustness ◽  
Global Network ◽  
Catch And Release ◽  
Animal Populations ◽  
Capture Recapture ◽  
Node Removal

Individuals can play different roles in maintaining connectivity and social cohesion in animal populations and thereby influence population robustness to perturbations. We performed a social network analysis in a reef shark population to assess the vulnerability of the global network to node removal under different scenarios. We found that the network was generally robust to the removal of nodes with high centrality. The network appeared also highly robust to experimental fishing. Individual shark catchability decreased as a function of experience, as revealed by comparing capture frequency and site presence. Altogether, these features suggest that individuals learnt to avoid capture, which ultimately increased network robustness to experimental catch-and-release. Our results also suggest that some caution must be taken when using capture–recapture models often used to assess population size as assumptions (such as equal probabilities of capture and recapture) may be violated by individual learning to escape recapture.

Social Network Analysis and Communities of Practice

2011 ◽  
pp. 474-480 ◽  
Author(s):  
Dimitris Asimakopoulos ◽  
Jie Yan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Science ◽  
Communities Of Practice ◽  
Empirical Research ◽  
Historical Perspective ◽  
Social Science Research ◽  
Science Research ◽  
Software Packages

Social network analysis (Scott, 2000; Wasserman & Faust, 1994) is a relatively new theory and methodology that has found wide application in social science research. In the early 2000s, an increasing number of scholars have been interested in computerized Social Network Analysis (SNA) and have adopted social network theory and techniques to study communities of practice (CoPs). In this article, the authors introduce SNA from a historical perspective, compare SNA with non-network theories and methods, and introduce popular SNA software packages. With reference to recent empirical research, the authors discuss several areas in which SNA has been applied to CoP research.

scholarly journals Není hvězda jako hvězda: identifikace klíčových aktérů v sociálních sítích

2021 ◽  
Vol 18 (1) ◽  
pp. 101-120
Author(s):  
Tomáš Diviák
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Statistical Models ◽  
Statistical Modelling ◽  
Centrality Measures ◽  
Network Data ◽  
Potential Solution ◽  
Software Packages ◽  
Definition Of

The concept of centrality and centrality measures are well-known and frequently used in social network analysis. They are also implemented in numerous software packages. However, that does not mean that it is easy to apply them correctly. This paper aims to introduce the most frequently used centrality measures, but more importantly to point out the problems related to their application and to sketch potential solutions for these problems. First, three basic centrality measures are introduced: degree, betweenness, and closeness. There are three broad categories of issues with centrality measures. These categories are: inadequate operationalisation of centrality measures, explanation of their distribution, and interdependence of observation in statistical modelling. A typology of flows in the network is presented as a potential solution allowing for transparent operationalisation. The so-called positional approach is another potential solution allowing for conceptually and computationally rigorous definition of centrality measures. Lastly, statistical models for network data are introduced as a way to deal with interdependence of observations. In the conclusion, challenges for measuring centrality in bipartite and multiplex networks are discussed.

scholarly journals Analysis of Social Networks Using an Information and Analytical System

2021 ◽  
Vol 110 ◽  
pp. 05009
Author(s):  
Rodion Filippov ◽  
Yuriy Leonov ◽  
Aleksandr Kuzmenko ◽  
Timofey Shestakov
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Monitoring ◽  
Automate Data ◽  
The Subject ◽  
Analytical System ◽  
Distinguishing Features ◽  
Analysis Of Social Networks

The subject of the study is the analysis of social networks and the construction of an information and analytical system to automate data monitoring and mining. Modern social network analysis systems are reviewed, and the distinguishing features of these systems are given. Various methods of social network analysis and tasks that can be solved using these methods are described. The effectiveness of the methods to determine the text sentiment is compared.

scholarly journals Examining the how of Plural Policing: Moving from Normative Debate to Empirical Enquiry

2020 ◽  
Vol 60 (3) ◽  
pp. 681-702 ◽  
Author(s):  
Martin Nøkleberg
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Current Literature ◽  
Network Structure ◽  
Mixed Economy ◽  
Distribution Of Power ◽  
Analytical Tools ◽  
Empirical Approaches

Abstract The networked and plural nature of policing suggests that agencies are often involved in extensive exchanges of expertise, resources and knowledge. However, the network structure and distribution of power between various policing actors can vary considerably. This highlights the importance of developing sound analytical perspectives that can help unpack the complexities behind the linkages. Applying the network perspective, this article underlines the value of utilizing analytical tools and approaches drawn from social network analysis, such as brokerage and homophily, to empirically assess the roles of agencies and their contribution to plural policing. This, in turn, shows how, in the mixed economy of policing, as well as being understood in terms of the normative debates that often figure in the current literature, relational phenomena also require more sophisticated empirical approaches.

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