Regularized modularity eigenmap for community discovery with side information

We propose a method for community discovery with side information based on a regularized modularity eigenmap. Community discovery has been conducted mostly based solely on the connectivity relation among nodes in a social network. However, the connectivity may change in time, or some links might be missing. Even when the connectivity relation in a network is only partially available, if other information about the network is available, it can be exploited as auxiliary or side information for community discovery. Our approach constructs a graph structure based on the side information so that both connectivity relation and side information can be uniformly dealt with in terms of graph representation. An objective function with a regularization term for the side information is proposed based the modularity matrix of a network. Extensive experiments are conducted over social network datasets and comparison with several state-of-the-art methods is reported.