Block Modelling and Learning for Structure Analysis of Networks with Positive and Negative Links

  • Xuehua Zhao
  • Hua Chen
  • Xueyan Liu
  • Xu TanEmail author
  • Wenzhuo Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)


Currently, many community mining methods for signed networks with positive and negative links have been proposed, however, these methods can only efficiently find the community of signed networks and unable to find other structure, such as bipartite, multipartite and so on. In this study, we present a mathematically principled community mining method for signed networks. Firstly, a probabilistic model is proposed to model the signed networks. Secondly, a variational Bayesian approach is deduced to learn the proximation distribution of model parameters. In our experiments, the proposed method is validated in the synthetic and real-word signed networks. The experimental results show the proposed method not only can efficiently find communities of signed networks but also can find the other structure.


Social networks Community mining Block modelling 



This work is funded by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (17YJCZH261, 17YJCZH157), National Science Foundation of China (61571444), Guangdong Province Natural Science Foundation (2016A030310072), Special Innovation Project of Guangdong Education Department (2017GKTSCX063), and Special Funds for the Cultivation of Scientific and Technological Innovation for College Students in Guangdong (pdjh2018b0862).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xuehua Zhao
    • 1
  • Hua Chen
    • 1
  • Xueyan Liu
    • 2
  • Xu Tan
    • 3
    Email author
  • Wenzhuo Song
    • 2
  1. 1.School of Digital MediaShenzhen Institute of Information TechnologyShenzhenChina
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchunChina
  3. 3.School of Software EngineeringShenzhen Institute of Information TechnologyShenzhenChina

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