Book ChapterDOI
Clustering 1-Dimensional Periodic Network Using Betweenness Centrality
Norie Fu,Vorapong Suppakitpaisarn,Vorapong Suppakitpaisarn +2 more
- pp 128-139
TLDR
A pseudo polynomial-time algorithm for temporal networks, of which the transit value is always positive and the least common divisor of all transit values is bounded, and shows that the centrality of networks with 125 nodes and 455 edges can be efficiently computed in 3.2 seconds.Abstract:
In this paper, we propose a clustering method based on the infinite betweenness centrality for temporal networks specified by 1-dimensional periodic graphs. While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them. We give a pseudo polynomial-time algorithm for temporal networks, of which the transit value is always positive and the least common divisor of all transit values is bounded. Our experimental results show that the centrality of networks with 125 nodes and 455 edges can be efficiently computed in 3.2 seconds. Not only the clustering results using the infinite betweenness centrality for this kind of networks are better, but also the nodes with biggest influence are more precisely detected when the betweenness centrality is computed over the periodic graph.read more
Citations
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Proceedings ArticleDOI
Random graphs
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Journal ArticleDOI
Clustering 1-dimensional periodic network using betweenness centrality.
TL;DR: A clustering pseudo-polynomial time algorithm for temporal networks, in which the transit value is always positive and the least common multiple of all transit values is bounded, which provides a better result for temporal social networks with an acceptable running time.
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TL;DR: This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
Journal ArticleDOI
A Set of Measures of Centrality Based on Betweenness
TL;DR: A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced in this paper, which define centrality in terms of the degree to which a point falls on the shortest path between others and there fore has a potential for control of communication.