Abstract
Community detection is the fundamental problem in the analysis and understanding of complex networks, which has attracted a lot of attention in the last decade. Active learning aims to achieve high accuracy using as few labeled data as possible. However, so far as we know, active learning has not been applied to detect community to improve the performance of discovering community structure of complex networks. In this paper, we propose a community detection algorithm called active semi-supervised community detection algorithm with label propagation. Firstly, we transform a given complex network into a weighted network, select some informative nodes using the weighted shortest path method, and label those nodes for community detection. Secondly, we utilize the labeled nodes to expand the labeled nodes set by propagating the labels of the labeled nodes according to an adaptive threshold. Thirdly, we deal with the rest of unlabeled nodes. Finally, we demonstrate our community detection algorithm with three real networks and one synthetic network. Experimental results show that our active semi-supervised method achieves a better performance compared with some other community detection algorithms.
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References
Grvan, M., Newman, M.E.J.: Community Structure in Social and Biological Networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)
Brandes, U.: On Variants of Shortest-path Betweenness Centrality and Their Generic Computation. Social Networks 30, 136–145 (2008)
Newman, M.E.J., Girvan, M.: Finding and Evaluating Community Structure in Networks. Phys. Rev. EÂ 69, 026113 (2004)
Comellas, F., Miralles, A.: A Fast and Efficient Algorithm to Identify Clusters in Networks. Appl. Math. Comput. 217, 2007–2014 (2010)
Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On Finding Graph Clusterings with Maximum Modularity. In: Brandstädt, A., Kratsch, D., Müller, H. (eds.) WG 2007. LNCS, vol. 4769, pp. 121–132. Springer, Heidelberg (2007)
Chen, W., Liu, Z., Sun, X., Wang, Y.: A Game-theoretic Framework to Identify Overlapping Communities in Social Network. Data Min. Knowl. Discov. 21, 224–240 (2010)
Raghavan, U.N., Albert, R., Kumara, S.: Near Linear Time Algorithm to Detect Community Structures in Large-scale Networks. Phys. Rev. EÂ 76, 036106 (2007)
Liu, X., Murata, T.: How Does Label Propagation Algorithm Work in Bipartite Networks? In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, pp. 5–8. IEEE CPS Press, Piscataway (2009)
Barber, M.J., Clark, J.W.: Detecting Network Communities By Propagating Labels Under Constraints. Phys. Rev. EÂ 80, 026129 (2009)
Liu, X., Murata, T.: Advanced Modularity-Specialized Label Propagation Algorithm for Detecting Communities in Networks. Physica A 389, 1493–1500 (2010)
Xie, J., Szymanski, B.K.: Community Detection Using a Neighborhood Strength Driven Label Propagation Algorithm. In: 1st International Network Science Workshop, pp. 188–195. IEEE CPS Press, Piscataway (2011)
Šubelj, L., Bajec, M.: Unfolding Network Communities by Combining Defensive and Offensive Label Propagation. In: International Workshop on the Analysis of Complex Networks, Catalonia, pp. 87–104 (2010)
Ma, X., Gao, L., Yong, X., Fu, L.: Semi-Supervised Clustering Algorithm for Community Structure Detection in Complex Networks. Physica A 389(1), 187–197 (2010)
Silva, T.C., Zhao, L.: Semi-Supervised Learning Guided by The Modularity Measure in Complex Networks. Neurocomputing 78(1), 30–37 (2012)
Scheffer, T., Wrobel, S.: Active Learning of Partially Hidden Markov Models. In: 12th ECML/PKDD Workshop on Instance Selection, Freiburg (2001)
Melville, P., Mooney, R.J.: Diverse Ensembles for Active Learning. In: 21st International Conference on Machine Learning, pp. 584–591. ACM Press, New York (2004)
Dasgupta, S., Hsu, D., Monteleoni, C.: A General Agnostic Active Learning Algorithm. In: Advances in Neural Information Processing Systems, pp. 353–360 (2008)
Nguyen, H.T., Smeulders, A.: Active Learning Using Pre-Clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning, pp. 623–630 (2004)
Vu, V.V., Labroche, N., Meunier, B.B.: Active Learning for Semi-Supervised K-Means Clustering. In: 22nd International Conference on Tools with Artificial Intelligence, pp. 12–15. IEEE CPS Press, Piscataway (2010)
Zhao, W., He, Q., Ma, H., Shi, Z.: Effective Semi-Supervised Document Clustering Via Active Learning with Instance-Level Constraints. Knowl. Inf. Syst. 30(3), 569–587 (2012)
Grira, N., Crucianu, M., Boujemaa, N.: Active Semi-Supervised Fuzzy Clustering. Pattern Recogn. 41(5), 1834–1844 (2008)
Wang, X., Davidson, I.: Active Spectral Clustering. In: 2010 IEEE International Conference on Data Mining, pp. 561–568 (2010)
Mallapragada, P.K., Jin, R., Jain, A.K.: Active Query Selection for Semi-Supervised Clustering. In: 19th International Conference on Pattern Recognition, pp. 1–4. IEEE Press, Piscataway (2008)
Huang, R., Lam, W., Zhang, Z.: Active Learning of Constraints for Semi-Supervised Text Clustering. In: 7th SIAM International Conference on Data Mining, pp. 113–124. Society for Industrial and Applied Mathematics Publications Press, Philadelphia (2007)
Zachary, W.W.: An Information Flow Model for Conflict and Fission in Small Groups. J. Anthropol. Res. 33(4), 452–473 (1977)
Lancichinetti, L., Fortunato, F., Radicchi, R.: Benchmark graphs for testing community detection algorithms. Phys. Rev. EÂ 78, 046110 (2008)
Newman, M.E.J.: Fast Algorithm for Detecting Community Structure in Networks. Phys. Rev. EÂ 69, 66133 (2004)
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Leng, M., Yao, Y., Cheng, J., Lv, W., Chen, X. (2013). Active Semi-supervised Community Detection Algorithm with Label Propagation. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_25
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DOI: https://doi.org/10.1007/978-3-642-37450-0_25
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