Abstract
Community detection problem has been studied for years, but no agreement on the common definition of community has been reached till now. One topology is found to be of different types, unipartite or bipartite/multipartite. The previous literatures mostly proposed only one type of community. As the definition of community is understood differently, the grouping results are always applicable to some specified networks. If the definition changes, the grouping result will no longer be “good”. In the present paper, it’s found that some vertices “must be” in the same community, while some maybe in or not in the same community. To do the community detection on mixed protein-protein interaction (PPI) networks, an energy model with two steps is proposed. First, vertices that “must be” in the same community are grouped together by properties; second, overlapping vertices are found by functions. In the energy model, “positive energy” is defined by attraction generated between two vertices, and “negative energy” by the attraction, which weakens the attraction between the corresponding two vertices, resulting from other vertices. Energy between two vertices is the sum of their positive and negative energy. Computing the energy of each community, the community structure can be found when maximum value of the energy sum of these communities is obtained. The model is utilized to find community structure in PPI networks. The results show that the energy model is applicable to unipartite, bipartite or mixed PPI networks. Vertices with similar property/roles in the same community and overlapping vertices are found for the network.
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References
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)
Przulj, N.: Protein-protein interactions: making sense of networks via graph theoretic modeling. BioEssays 33(2), 115–123 (2011)
Lee, K., Chuang, H., Beyer, A., et al.: Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species. Nucl. Acids Res. 36, e136 (2008)
Ulitsky, I., Shamir, R.: Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 25, 1158–1164 (2009)
Wang, C., Ding, C., Yang, Q., et al.: Consistent dissection of the protein interaction network by combining global and local metrics. Genome Biol. 8, R271 (2007)
Wang, J., Du, Z., Payattakool, R., et al.: Recent advances in clustering methods for protein interaction networks. BMC Genomics 11(Suppl 3), S10 (2010)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Nat. Acad. Sci. U.S.A. 100(21), 12123–12128 (2003)
Gillis, J., Pavlidis, P.: The impact of multifunctional genes on “Guilt by Association” analysis. PLoS One 6(2), 16 (2011)
Davis, B.B., Garder, M.R.: Deep South. University of Chicago Press, USA (1941)
Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations - can geographic isolation explain this unique trait. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)
Zachary, W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)
Scott, J., Hughes, M.: The Anatomy of Scottish Capital: Scottish Companies and Scottish Capital. Croom Helm, London (1980)
Zhou, T., Lu, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)
Leicht, E.A., Holme, P., Newman, M.E.J.: Vertex similarity in networks. Eur. Phys. J. E73, 026120 (2006)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008, P10008 (2008). Email alert: RSS feed
Lee, C., Reid, F., McDaid, A., Hurley, N.: Detecting highly overlapping community structure by greedy clique expansion. In: SNA-KDD 2010, pp. 33–42, February 2010
Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(9), 814–818 (2005)
Reguly, T., Breitkreutz, A., Boucher, L., et al.: Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae. J. Biol. 5(4), 11 (2006)
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Pang, Y., Bai, L., Bu, K. (2015). An Energy Model for Detecting Community in PPI Networks. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_9
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DOI: https://doi.org/10.1007/978-3-319-22849-5_9
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