Abstract
Community structure is a hallmark of a variety of real-world networks. Here we propose a local method for detecting communities in networks. The method is described as ‘local’ because it is intended to find the community to which a given source node belongs without knowing all the communities in the network. We have devised this method inspired by possible mechanisms for stable propagation of neuronal activities in neural networks. To demonstrate the effectiveness of our method, local detection of communities in synthetic benchmark networks and real social networks is examined. The community structure detected by our method is perfectly consistent with the correct community structure of these networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Newman, M.E.J.: Communities, modules and large-scale structure in networks. Nature Phys. 8, 25–31 (2012)
Bagrow, J.P., Bollt, E.M.: Local method for detecting communities. Phys. Rev. E 72, 046108 (2005)
Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72, 026132 (2005)
Luo, F., Wang, J., Promislow, E.: Exploring local community structures in large networks. Web Intelligence and Agent Systems 6, 387–400 (2008)
Chen, J., Zaïane, O., Goebel, R.: Local community identification in social networks. In: International Conference on Advances in Social Network Analysis and Mining (ASONAM 2009), pp. 237–242 (2009)
Lancichinetti, A., Fortunato, S., Kertesz, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11, 033015 (2009)
Chen, Q., Wu, T.-T., Fang, M.: Detecting local community structures in complex networks based on local degree central nodes. Physica A 392, 529–537 (2013)
Tuckwell, H.: Introduction to theoretical neurobiology: vol. 2 nonlinear and stochastic theories. Cambridge University Press (1988)
Collins, A.M., Loftus, E.F.: Spreading-Activation Theory of Semantic Processing. Psychological Review 82, 407–428 (1975)
Okamoto, H.: Topic-dependent document ranking: citation network analysis by analogy to memory retrieval in the brain. In: Honkela, T. (ed.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 371–378. Springer, Heidelberg (2011)
Branting, L.K.: Context-sensitive detection of local community structure. Soc. Netw. Anal. Min. 2, 279–289 (2012)
Arenas, A., Diaz-Guilera, A., Perez-Vicente, C.J.: Synchronization reveals topological scales in complex networks. Phys. Rev. Lett. 96, 114102 (2006)
Arenas, A., Diaz-Guilera, A., Perez-Vicente, C.J.: Synchroization processes in complex networks. Physica D 224, 27–34 (2006)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms Phys. Rev. E 78, 046110 (2008)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 291–473 (1977)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Stanford Digital Library Technologies Project (1998), http://google.stanford.edu/~backrub/pageranksub.ps
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Okamoto, H. (2013). Local Detection of Communities by Neural-Network Dynamics. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-40728-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
eBook Packages: Computer ScienceComputer Science (R0)