Link prediction in complex networks via modularity-based belief propagation

doi:10.1088/1674-1056/26/3/038902

Abstract Link prediction aims at detecting missing, spurious or evolving links in a network, based on the topological information and/or nodes' attributes of the network. Under the assumption that the likelihood of the existence of a link between two nodes can be captured by nodes' similarity, several methods have been proposed to compute similarity directly or indirectly, with information on node degree. However, correctly predicting links is also crucial in revealing the link formation mechanisms and thus in providing more accurate modeling for networks. We here propose a novel method to predict links by incorporating stochastic-block-model link generating mechanisms with node degree. The proposed method first recovers the underlying block structure of a network by modularity-based belief propagation, and based on the recovered block structural information it models the link likelihood between two nodes to match the degree sequence of the network. Experiments on a set of real-world networks and synthetic networks generated by stochastic block model show that our proposed method is effective in detecting missing, spurious or evolving links of networks that can be well modeled by a stochastic block model. This approach efficiently complements the toolbox for complex network analysis, offering a novel tool to model links in stochastic block model networks that are fundamental in the modeling of real world complex networks.

Keywords: link prediction complex network belief propagation modularity

Received: 26 August 2016
Revised: 25 November 2016
Accepted manuscript online:

PACS:	89.75.Hc	(Networks and genealogical trees)
	02.50.Tt	(Inference methods)
	89.20.Ff	(Computer science and technology)

Fund: Project supported by the National Natural Science Foundation of China (Grants No. 61202262), the Natural Science Foundation of Jiangsu Province, China (Grants No. BK2012328), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grants No. 20120092120034).

Corresponding Authors: Darong Lai
E-mail: darong.lai@gmail.com

Cite this article:

Darong Lai(赖大荣), Xin Shu(舒欣), Christine Nardini Link prediction in complex networks via modularity-based belief propagation 2017 Chin. Phys. B 26 038902

[1]	Newman M E J 2010 Networks: An Introduction, 1st edn. (Oxford: Oxford University Press) p. 1
[2]	Ren T, Wang Y F, Liu M M and Xu Y J 2016 Chin. Phys. B 25 020101
[3]	Boccaletti S, Latora V, Moreno Y, Chavez M and Hwang D U 2006 Phys. Rep. 424 175
[4]	Ruan Y R, Lao S Y, Xiao Y D, Wang J D and Bai L 2016 Chin. Phys. Lett. 33 028901
[5]	Duan J M, Shang M S, Cai S M and Zhang Y X 2015 Acta Phys. Sin. 64 200501 (in Chinese)
[6]	Xu X L, Liu C P and He D R 2016 Chin. Phys. Lett. 33 048901
[7]	Yu H, Braun P, Yildirim M A et al. 2008 Science 322 104
[8]	Stumpf M P H, Thorne T, de Silva E, Stewart R, An H J, Lappe M and Wiuf C 2008 Proc. Natl. Acad. Sci. USA 105 6959
[9]	Amaral L A N 2008 Proc. Natl. Acad. Sci. USA 105 6795
[10]	Butts C 2003 Soc. Netw. 25 103
[11]	Kossinets G 2006 Soc. Netw. 28 247
[12]	Schifanella R, Barrat A, Cattuto C, Markines B and Menczer F 2010 Proceedings of the Third ACM International Conference on Web Search and Data Mining, February 3-6, 2010, New York, USA, p. 271
[13]	Fouss F, Pirotte A, Renders J M and Saerens M 2007 IEEE T. Knowl. Data En. 19 355
[14]	Lü L and Zhou T 2011 Physica A 390 1150
[15]	Newman M E J 2001 Phys. Rev. E 64 025102
[16]	Adamic L A and Adar E 2003 Soc. Netw. 25 211
[17]	Zhou T, Lü L and Zhang Y C 2009 Eur. Phys. J. B 71 623
[18]	Clauset A, Moore C and Newman M E J 2008 Nature 453 98
[19]	Guimera R and Sales-Pardo M 2009 Proc. Natl. Acad. Sci. USA 106 22073
[20]	Pan L, Zhou T, Lü L and Hu C K 2016 Sci. Rep. 6 22955
[21]	Taskar B, Wong M F, Abbeel P and Koller D 2004 Proceedings of Neural Information Processing Systems, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada, p. 659
[22]	Yu K, Chu W, Yu S, Tresp V and Xu Z 2007 Proceedings of Neural Information Processing Systems, December 4-7, 2006, Vancouver, British Columbia, Canada, p. 1657
[23]	Li Y J, Yin C, Yu H and Liu Z 2016 Acta Phys. Sin. 65 200501 (in Chinese)
[24]	Katz L 1953 Psychmetrika 18 39
[25]	Salton G and McGill M J 1983 Introduction to Modern Information Retrieval (Auckland: McGraw-Hill) p. 1
[26]	Xie Y B, Zhou T and Wang B H 2008 Physica A 387 1683
[27]	Lü L, Jin C and Zhou T 2009 Phys. Rev. E 83 046122
[28]	Wang W Q, Zhang Q M and Zhou T 2012 Europhys. Lett. 98 28004
[29]	Zhang Q M, Xu X K, Zhu Y X and Zhou T 2015 Sci. Rep. 5 10350
[30]	Zhang Q M, Lü L, Wang W Q and Zhou T 2013 PloS One 8 e55437
[31]	Lü L, Pan L, Zhou T and Zhang Y C 2015 Proc. Natl. Acad. Sci. USA 112 2345
[32]	Girvan M and Newman M E J 2002 Proc. Natl Acad. Sci. USA 99 7821
[33]	Shen Y, Ren G, Liu Y and Xu J L 2016 Chin. Phys. B 25 068901
[34]	Nowicki K and Snijders T A B 2001 J. Am. Stat. Assoc. 96 1077
[35]	Karrer B and Newman M E J 2011 Phys. Rev. E 83 016107
[36]	Zhang P and Moore C 2014 Proc. Natl. Acad. Sci. USA 111 18144
[37]	Hanely J A and McNeil B J 1982 Radiology 143 29
[38]	Karrer B and Newman M E J 2011 Phys. Rev. E 83 016107
[39]	Lai D, Shu X and Nardini C 2016 J. Stat. Mech. P053301
[40]	Zachary W W 1977 J. Anthropol. Res. 33 452
[41]	Adamic L A and Glance N 2005 Proceedings of the 3rd International Workshop on Link Discovery, August 21-24, 2005, Chicago, USA, p. 36
[42]	Lusseau D, Schneider K, Boisseau O J, Haase P, Slooten E and Dawson S M 2003 Behav. Ecol. Sociobiol. 54 396
[43]	Ulanowicz R E, Bondavalli C and Egnotovich M S 1998 Technical Report Ref. No. [UMCES] CBL 98-123
[44]	Sun S, Ling L, Zhang N, Li G and Chen R 2003 Nucleic Acids Res. 31 2443
[45]	Danon L, Diaz-Guilera A, Duch J and Arenas A 2005 J. Stat. Mech. P09008
[46]	Fortunato S 2010 Phys. Rep. 486 75

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