Abstract
Community discovery in Social network is one of the hot spots. In real networks, some nodes belong to several different communities. Overlapping community discovery has been more and more popular. Label propagation algorithm has been proven to be an effective method for complex network community discovery, this algorithm has the characteristics of simple and fast. For the poor stability problem of Label propagation algorithm, this article proposes a stable overlapping communities discovery method based on the label propagation algorithm: SALPA. At the beginning of the method, introduce the influence of nodes, which is used to measure the influence of nodes, select the most influential nodes as the core nodes, in the propagating stage, when there are more than one label with the same degree of membership, select the connectivity lager than the threshold. The method has been carried out in three real networks and two big synthetic networks. Compared with the classical algorithm, experiment results demonstrate the effectiveness, stability and computational speed of the method have been improved.
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References
Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Lancichinetti, A., Radicchi, F., Ramasco, J.J., Fortunato, S.: Finding statistically significant communities in networks. PloS one 6(4), e18961 (2011)
Baumes, J., Goldberg, M., Magdon-Ismail, M.: Efficient Identification of Overlapping Communities. In: Kantor, P., Muresan, G., Roberts, F., Zeng, D.D., Wang, F.-Y., Chen, H., Merkle, R.C. (eds.) ISI 2005. LNCS, vol. 3495, pp. 27–36. Springer, Heidelberg (2005)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76(3), 036106 (2007)
Gregory, S.: Finding overlapping communities in networks by label propagation. New Journal of Physics 12(10), 103018 (2010)
Centola, D.: The spread of behavior in an online social network experiment. Science 329(5996), 1194–1197 (2010)
Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nature Physics 6(11), 888–893 (2010)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 452–473 (1977)
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. Behavioral Ecology and Sociobiology 54(4), 396–405 (2003)
Shen, H., Cheng, X., Cai, K., Hu, M.B.: Detect overlapping and hierarchical community structure in networks. Physica A: Statistical Mechanics and its Applications 388(8), 1706–1712 (2009)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11(3), 033015 (2009)
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Liu, B., Wang, C., Wang, C., Wang, Y. (2015). A Novel Algorithm for Finding Overlapping Communities in Networks Based on Label Propagation. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_35
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DOI: https://doi.org/10.1007/978-3-319-20472-7_35
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