Abstract
Various methods for analyzing networks have been proposed. Among them, methods for community detection based on network structures are important for making networks simple and easy to understand. As an attempt to incorporate background knowledge of given networks, a method known as constrained community detection has been proposed recently. Constrained community detection shows robust performance on noisy data since it uses background knowledge. In particular, methods for community detection based on constrained Hamiltonian have advantages of flexibility in output results. In this paper, we propose a method for accelerating the speed of constrained community detection based on Hamiltonian. Our optimization method is a variant of Blondel’s Louvain method which is well-known for its computational efficiency. Our experiments showed that our proposed method is superior in terms of computational time, and its accuracy is almost equal to the existing method based on simulated annealing under the same conditions. Our proposed method enables us to perform constrained community detection in larger networks compared with existing methods. Moreover, we compared the strategies of adding constraints incrementally in the process of constrained community detection.
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Adamic, L.A., Glance, N.: The political blogosphere and the 2004 u.s. election: Divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD 2005, pp. 36–43. ACM, New York (2005)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (2008)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Eaton, E., Mansbach, R.: A spin-glass model for semi-supervised community detection. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012), July 22-26, pp. 900–906. AAAI Press (2012)
Fortunato, S.: Community detection in graphs. Physics Reports 486(3-5), 75–174 (2010)
Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Krebs, V.: Books about us politics. Nodes represent books about US politics sold by the online bookseller Amazon.com. Edges represent frequent co-purchasing of books by the same buyers, as indicated by the “customers who bought this book also bought these other books” feature on Amazon
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1994, pp. 3–12. Springer-Verlag New York, Inc., New York (1994)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 26113 (2004)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab (November 1999) Previous number = SIDL-WP-1999-0120
Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P.: Community detection in social media. Data Mining and Knowledge Discovery 24(3), 515–554 (2012)
Porter, M.A., Onnela, J.-P., Mucha, P.J.: Communities in networks. Notices of the AMS 56(9) (2009)
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006)
Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research 3, 583–617 (2003)
Watts, D., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. CoRR, abs/1205.6233 (2012)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)
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Nakata, K., Murata, T. (2015). Fast Optimization of Hamiltonian for Constrained Community Detection. In: Mangioni, G., Simini, F., Uzzo, S., Wang, D. (eds) Complex Networks VI. Studies in Computational Intelligence, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-16112-9_8
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DOI: https://doi.org/10.1007/978-3-319-16112-9_8
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