Computer Science – Social and Information Networks
Scientific paper
2011-08-06
Proceedings of the 11th International Conference On Intelligent Systems Design And Applications, pp. 88-93, 2011
Computer Science
Social and Information Networks
6 pages, 1 figure, ISDA '11: 11th International Conference On Intelligent Systems Design And Applications, 2011
Scientific paper
10.1109/ISDA.2011.6121636
In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the k-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.
Ferrara Emilio
Fiumara Giacomo
Meo Pasquale de
Provetti Alessandro
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