Social Networks Analysis: Tools, Measures and Visualization

Ghali, Neveen I.; Panda, Mrutyunjaya; Hassanien, Aboul Ella; Abraham, Ajith; Snåšel, Václav

doi:10.1007/978-1-4471-4054-2_1

“…This is the ratio between the number of existing edges and the number of potentially possible directed or undirected edges in the network. The value is between 0 and 1 [80]. The edge density D can be represented by a division of the connections present in the network and the connections possible in the network (see formula 2):…”

Section: Edge Densitymentioning

confidence: 99%

Need for Standardization and Systematization of Test Data for Job-Shop Scheduling

Weber¹,

Tiefenbacher²,

Gronau³

2019

Data

2

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The development of new and better optimization and approximation methods for Job Shop Scheduling Problems (JSP) uses simulations to compare their performance. The test data required for this has an uncertain influence on the simulation results, because the feasable search space can be changed drastically by small variations of the initial problem model. Methods could benefit from this to varying degrees. This speaks in favor of defining standardized and reusable test data for JSP problem classes, which in turn requires a systematic describability of the test data in order to be able to compile problem adequate data sets. This article looks at the test data used for comparing methods by literature review. It also shows how and why the differences in test data have to be taken into account. From this, corresponding challenges are derived which the management of test data must face in the context of JSP research.

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“…Several real-world networks have been successfully modeled as undirected and directed graphs to study the intrinsic structural properties of the networks as well as the topological importance of nodes in these networks. The real-world networks that have been subjected to complex network analysis typically fall under one of these categories: social networks [1], transportation networks [2], biological networks [3], citation networks [4], co-authorship networks [5] and etc. One category of real-world networks for which sufficient attention has not yet been given are the regional networks featuring the states within a country.…”

Section: Introductionmentioning

confidence: 99%

Centrality Analysis of the United States Network Graph

Meghanathan

¹

2018

Encyclopedia of Information Science and Technology, Fourth Edition

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We model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. We determine a ranking of the states in the US with respect to the four commonly studied centrality metrics: degree, eigenvector, betweenness and closeness. We observe the states of Missouri and Maine to be respectively the most central state and the least central state with respect to all the four centrality metrics. The degree distribution is bi-modal Poisson. The eigenvector and closeness centralities also exhibit Poisson distribution, while the betweenness centrality exhibits power-law distribution. We observe a higher correlation in the ranking of vertices based on the degree centrality and betweenness centrality.

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“…Several real-world networks have been successfully modeled as undirected and directed graphs to study the intrinsic structural properties of the networks as well as the topological importance of nodes in these networks. The real-world networks that have been subjected to complex network analysis typically fall under one of these categories: social networks (Ghali et al, 2012), transportation networks (Cheung & Gunes, 2012), biological networks (Ma & Gao, 2012), citation networks (Zhao & Strotmann, 2015), co-authorship networks (Ding, 2011) and etc. One category of real-world networks for which sufficient attention has not yet been given are the regional networks featuring the states within a country.…”

Section: Introductionmentioning

confidence: 99%

Complex Network Analysis of the Contiguous United States Graph

Meghanathan¹

2017

CIS

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We model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. We determine a ranking of the states in the US with respect to a suite of node-level metrics: the centrality metrics (degree, eigenvector, betweenness and closeness), eccentricity, maximal clique size, and local clustering coefficient. We propose a normalization-based approach to obtain a comprehensive centrality ranking of the vertices (that is most likely to be tie-free) encompassing the normalized values of the four centrality metrics. We have applied the proposed normalization-based approach on the US States graph to obtain a tie-free ranking of the vertices based on a comprehensive centrality score. We observe the state of Missouri to be the most central state with respect to all the four centrality metrics. We have also analyzed the US States graph with respect to a suite of network-level metrics: bipartivity index, assortativity index, modularity, size of the minimum connected dominating set, algebraic connectivity and degree metrics. The approach taken in this paper could be useful for several application domains: transportation networks (to identify central hubs), politics (to identify campaign venues with larger geographic coverage), cultural and electoral studies (to identify communities of states that are relatively proximal to each other) and etc.

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Social Networks Analysis: Tools, Measures and Visualization

Cited by 27 publications

References 31 publications

Need for Standardization and Systematization of Test Data for Job-Shop Scheduling

Need for Standardization and Systematization of Test Data for Job-Shop Scheduling

Centrality Analysis of the United States Network Graph

Complex Network Analysis of the Contiguous United States Graph

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