Abstract
Community structure is one of the most important features of complex networks, it uncovers the internal organization of the nodes. Affinity propagation (AP) is a recent proposed powerful cluster algorithm as it costs much less time and reaches much lower error. But it was shown that AP displayed severe convergence problems for identifying communities on the majority of unweighted protein-protein interaction (PPI) networks. On the contrary, AP was shown to achieve great success for identifying communities in benchmark artificial and social networks. So, in this study, we use AP to identify communities on artificial, social and unweighted PPI networks for finding the problem of the conflict. And we compare AP with Markov cluster (MCL), which was shown to outperform a number of clustering algorithms for PPI networks. The experimental results have shown that AP performs well without oscillations when similarity matrixes are chosen properly, and MCL is more accurate than AP but it runs slower than AP in large scale networks.
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Jia, C., Jiang, Y., Yu, J. (2010). Affinity Propagation on Identifying Communities in Social and Biological Networks. In: Bi, Y., Williams, MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science(), vol 6291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15280-1_58
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DOI: https://doi.org/10.1007/978-3-642-15280-1_58
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