Combination of links and node contents for community discovery using a graph regularization approach

With the rapid growth of the networked data, the study of community detection is drawing increasing attention of researchers. A number of algorithms have been proposed and some of them have been well applied in many research fields, such as recommendation systems, information retrieval, etc. Traditionally, the community detection methods mainly use the knowledge of the topological structure which contains the most important clue for finding potential groups or communities. However, as we know, a wealth of content information exists on the nodes in real-world networks, and may help for community detection. Considering the above problem, we introduce a novel community detection method under the framework of nonnegative matrix factorization (NMF), and adopt the idea that two nodes with similar content will be most likely to belong to the same community to achieve the incorporation of links and node contents, i.e., we employ a graph regularization to penalize the dissimilarity of nodes denoted by community memberships. Besides, we introduce an intuitive manifold learning strategy to recover the intrinsic geometrical structure of the content information, i.e., K-near neighbor consistency. In addition, we found that, there are still drawbacks in this framework due to it does not consider the heterogeneous distribution of node degrees. This heterogeneous distribution can affect the function of graph regularization and isolates the original community memberships. We first proposed the node popularities satisfying the above interpretation and develop a new NMF-based model, named as Combination of Links and Node Contents for Community Discovery (CLNCCD). The experiments on both artificial and real-world networks compared with the state-of-the-art methods show that, the new model obtains significant improvement for community detection by incorporating node contents effectively.