Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anchuri, P., Magdon-Ismail, M.: Communities and balance in signed networks: a spectral approach. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 235–242. IEEE (2012)
Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational Inference: A Review for Statisticians. ArXiv e-prints, January 2016
Doreian, P., Mrvar, A.: A partitioning approach to structural balance. Soc. Netw. 18(2), 149–168 (1996)
Ghoshal, G., Mangioni, G., Menezes, R., Poncela-Casanovas, J.: Social system as complex networks. Soc. Netw. Anal. Min. 4(1), 1–2 (2014)
Kropivnik, S., Mrvar, A.: An analysis of the slovene parliamentary parties network. In: Developments in Statistics and Methodology, pp. 209–216 (1996)
Liu, X., Wang, W., He, D., Jiao, P., Jin, D., Cannistraci, C.V.: Semi-supervised community detection based on non-negative matrix factorization with node popularity. Inf. Sci. 381, 304–321 (2017)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge (2012)
Newman, M.: Networks: An Introduction. Oxford University Press, Oxford (2010)
Newman, M.E.: Communities, modules and large-scale structure in networks. Nat. Phys. 8(1), 25–31 (2012)
Read, K.E.: Cultures of the central highlands, New Guinea. Southwest. J. Anthropol. 10(1), 1–43 (1954)
Traag, V.A., Bruggeman, J.: Community detection in networks with positive and negative links. Phys. Rev. E 80(3), 036115 (2009)
Yang, B., Cheung, W., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007)
Yang, B., Liu, X., Li, Y., Zhao, X.: Stochastic blockmodeling and variational bayes learning for signed network analysis. IEEE Trans. Knowl. Data Eng. PP(99), 1 (2017)
Zhao, X., Yang, B., Liu, X., Chen, H.: Statistical inference for community detection in signed networks. Phys. Rev. E 95(4), 042313 (2017)
Acknowledgments
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, X., Chen, H., Liu, X., Tan, X., Song, W. (2018). Block Modelling and Learning for Structure Analysis of Networks with Positive and Negative Links. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-99247-1_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99246-4
Online ISBN: 978-3-319-99247-1
eBook Packages: Computer ScienceComputer Science (R0)