Abstract
Multidimensional networks, networks with multiple kinds of relations, widely exist in various fields. Structure exploration (i.e., structural regularity exploration) is one fundamental task of network analysis. Most existing structural regularity exploration methods for multidimensional networks need to pre-assume which type of structure they have, and some methods that do not need to pre-assume the structure type usually perform poorly. To explore structural regularities in multidimensional networks well without pre-assuming which type of structure they have, we propose a novel feature aggregation method based on a mixture model and Bayesian theory, called the multidimensional Bayesian mixture (MBM) model. Experiments conducted on a number of synthetic and real multidimensional networks show that the MBM model achieves better performance than other relative models on most networks.
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
Tang, L., Wang, X.F., Liu, H.: Community detection in multi-dimensional networks. Technical report, DTIC Document, January 2010
Newman, M.E.J., Leicht, E.A.: Mixture models and exploratory analysis in networks. Proc. Natl. Acad. Sci. 104(23), 9564–9569 (2007)
Chai, B.-F., Yu, J., Jia, C.-Y., Yang, T.-B., Jiang, Y.-W.: Combining a popularity-productivity stochastic block model with a discriminative-content model for general structure detection. Phys. Rev. E 88(1), 012807 (2013)
Zhu, G., Li, K.: A unified model for community detection of multiplex networks. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014, Part I. LNCS, vol. 8786, pp. 31–46. Springer, Heidelberg (2014)
Tang, L., Liu, H.: Uncovering cross-dimension group structures in multi-dimensional networks. In: SDM Workshop on Analysis of Dynamic Networks, Sparks, NV (2009)
Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.-P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980), 876–878 (2010)
DuBois, C., Smyth, P.: Modeling relational events via latent classes. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 803–812, Washington, USA (2010)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B 39, 1–38 (1977)
Neal, R.M.: Slice sampling. Ann. Stat. 31, 705–741 (2003)
Ana, L., Jain, A.K.: Robust data clustering. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 128–133, Madison, USA (2003)
Acknowledgements
This paper is supported in part by grants: National 863 Program of China (2015AA015405), NSFCs (National Natural Science Foundation of China) (61402128, 61473101, 61173075 and 61272383), Strategic Emerging Industry Development Special Funds of Shenzhen (JCYJ20140508161040764 and JCYJ20140417172417105) and Scientific Research Foundation in Shenzhen (Grant No. JCYJ20140627163809422).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, Y., Wang, X., Tang, B., Bu, J., Chen, Q., Xiang, X. (2015). Structural Regularity Exploration in Multidimensional Networks. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-26555-1_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26554-4
Online ISBN: 978-3-319-26555-1
eBook Packages: Computer ScienceComputer Science (R0)