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Chin. Phys. B, 2017, Vol. 26(3): 038902    DOI: 10.1088/1674-1056/26/3/038902
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

Link prediction in complex networks via modularity-based belief propagation

Darong Lai(赖大荣)1,2, Xin Shu(舒欣)3, Christine Nardini4,5
1 School of Computer Science and Engineering, Southeast University, Nanjing 211189, China;
2 Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing 210096, China;
3 College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;
4 CNR-IAC "Mauro Picone" Via dei Taurini 19, 00185 Roma, Italy;
5 Personalgenomics, Via delle Grazie, Verona, Italy
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      Published:  05 March 2017
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

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