Community-Based Link Prediction in Social Networks

  • Rong KuangEmail author
  • Qun Liu
  • Hong Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9713)


Link prediction has attracted wide attention in the related fields of social networks which has been widely used in many domains, such as, identifying spurious interactions, extracting missing information, evaluating evolving mechanism of complex networks. But all of the previous works do not considering the influence of the neighbors and just applying in small networks. In this paper, a new similarity algorithm is proposed, which is motivated by the herd phenomenon taking place on network. Moreover, it is found that many links are assigned low scores while it has a longer path. Therefore, if such links the longer path has not been taken into account, which can improve the efficiency of time further, especially in large-scale networks. Extensive experiments were conducted on five real-world social networks, compared with the representative node similarity-based methods, our proposed model can provide more accurate predictions.


Link prediction Community structure Common neighbor Herd phenomenon 



This work is partly funded by the National Nature Science Foundation of China (61075019) and the Natural Science Foundation of Chongqing (CSTC2014jcyjA40047) and the Municipal Education Commission research project of Chongqing (KJ1400403).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of China

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