Abstract
We propose algorithms for the detection of disjoint and overlapping communities in networks. The algorithms exploit both the degree and clustering coefficient of vertices as these metrics characterize dense connections, which we hypothesize as being indicative of communities. Each vertex independently seeks the community to which it belongs, by visiting its neighboring vertices and choosing its peers on the basis of their degrees and clustering coefficients. The algorithms are intrinsically data parallel. We devise a version for Graphics Processing Unit (GPU). We empirically evaluate the performance of our methods. We measure and compare their efficiency and effectiveness to several state-of-the-art community detection algorithms. Effectiveness is quantified by metrics, namely, modularity, conductance, internal density, cut ratio, weighted community clustering and normalized mutual information. Additionally, average community size and community size distribution are measured. Efficiency is measured by the running time. We show that our methods are both effective and efficient. Meanwhile, the opportunity to parallelize our algorithm yields an efficient solution to the community detection problem.
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Song, Y., Bressan, S., Dobbie, G. (2015). Fast Disjoint and Overlapping Community Detection. In: Hameurlain, A., Küng, J., Wagner, R., Decker, H., Lhotska, L., Link, S. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII. Lecture Notes in Computer Science(), vol 8980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46485-4_6
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