Abstract
The progressively scale of online social network leads to the difficulty of traditional algorithms on detecting communities. We introduce an efficient and fast algorithm to detect community structure in social networks. Instead of using the eigenvectors in spectral clustering algorithms, we construct a target function for detecting communities. The whole social network communities will be partitioned by this target function. We also analyze and estimate the generalization error of the algorithm. The performance of the algorithm is compared with the standard spectral clustering algorithm, which is applied to different well-known instances of social networks with a community structure, both computer generated and from the real world. The experimental results demonstrate the effectiveness of the algorithm.
Similar content being viewed by others
References
Aggyriou A, Herbster M, Pontil M (1950) Combing graph laplacians for semi-supervised learning. Trans Am Math Soc 68: 337–404
Barthélemy M, Fortunato S (2007) Resolution limit in community detection. Proc Natl Acad Sci 104: 36–41
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7: 2399–2434
Belkin M, Matveeva I, Niyogi P (2004) Regularization and semi-supervised learning on large graphs. In: Shawe-Taylor J, Singer Y (eds). Proceedings of the 17th annual conference on learning theory, pp. 624–638
Boisseau OJ, Dawson SM, Haase P et al (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54: 396–405
Cao Y, Chen DR (2011) Consistency of regularized spectral clustering. Appl Comput Harmon Anal 30: 319–336
Chen H, Li LQ, Peng JT (2009) Error bounds of muti-graph regularized semi-supervised classfication. Inf Sci 179: 1960–1969
Chen LN, Li ZP, Zhang SH et al (2008) Quantitative function for community detection. Phys Rev E 77: 036109
Cheng B, Huang TS, Yang JC, Yan SC (2010) Learning With ℓ1-graph for image analysis. IEEE Trans Image Process 19: 858–866
Chung FRK (1997) Spectral graph theory. AMS Press, Providence, R.I
Cucker F, Zhou DX (2007) Learning theory: an approximation theory viewpoint. Cambridge University Press, Cambridge
De la Peña KVH, Giné E (1999) Decoupling: from dependence to independence. Springer, New York
Donetti L, Munoz MA (2004) Detecting network communities: a new systematic and efficient algorithm. J Stat Mech: Theor Exp, P10012
Fortunato S, Latora V, Marchiori M (2004) Method to find community structures based on information centrality. Phys Rev E 70: 056104
Fortunato S (2010) Community detection in graphs. Phys Rep 486: 75–174
Hoeffding W (1963) Probability inequalities for sums of bounded random variables. J Am Stat Assoc 58: 13–30
Lee CH, Zaïane OR, Park HH et al (2008) Clustering high dimensional data: a graph-based relaxed optimization approach. Inf Sci 178: 4501–4511
Lugesi G, Stéphan S, Voyatis N (2008) Ranking and empirical minimization of U-statistics. Ann Stat 36: 844–874
Luxburg UV (2006) A tutorial on spectral clustering. Stat Comput 17(4): 395–416
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69: 026113
Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69: 066133
Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393: 440–442
Xing EP, Ng AY, Jordan MI (2003) Distance metric learning, with application to clustering with side-information. Adv Neural Inf Process Syst 15
Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33: 452–473
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Huang, L., Li, R., Chen, H. et al. Detecting network communities using regularized spectral clustering algorithm. Artif Intell Rev 41, 579–594 (2014). https://doi.org/10.1007/s10462-012-9325-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-012-9325-3