@inproceedings{3e355bca8c304a5a83ba59285b2e86d6,
title = "Structural regularity exploration in multidimensional networks",
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.",
keywords = "Bayesian theory, Mixture model, Multidimensional networks, Network structure, Structural regularity exploration",
author = "Yi Chen and Xiaolong Wang and Buzhou Tang and Junzhao Bu and Qingcai Chen and Xin Xiang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-26555-1\_60",
language = "英语",
isbn = "9783319265544",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "532--540",
editor = "Tingwen Huang and Qingshan Liu and Lai, \{Weng Kin\} and Sabri Arik",
booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",
address = "德国",
}