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.