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Clustering 1-dimensional periodic network using betweenness centrality
Norie Fu1, Vorapong Suppakitpaisarn1,2
1JST, ERATO Kawarabayashi Large Graph Project, Global Research Center for Big Data Mathematics, National Institute of Informatics (NII), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-0003 Japan.
Background:
While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them.
Methods:
Based on betweenness centrality for periodic graphs, we give 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.
Results:
Our experimental results show that the centrality of networks with 125 nodes and 455 edges can be efficiently computed in 3.2 s. Not only the clustering results using the infinite betweenness centrality for this kind of networks are better, but also the nodes with biggest influences are more precisely detected when the betweenness centrality is computed over the periodic graph.
Conclusion:
The algorithm provides a better result for temporal social networks with an acceptable running time.
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