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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-319-26555-1_60
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