A non-negative matrix factorization approach to update communities in temporal networks using node features

Community detection looks for groups of nodes in networks, mainly using network topological, link-based features, not taking into account features associated with each node. Clustering algorithms, on the other hand, look for groups of objects using features describing each object. Recently, link features and node attributes have been combined to improve community detection. Community detection methods can be designed to identify communities that are disjoint or overlapping, crisp or soft and static or dynamic. In this paper, we propose a dynamic community detection method for finding soft overlapping groups in temporal networks with node attributes. Our approach is based on a non-negative matrix factorization model that uses automatic relevance determination to detect the number of communities. Preliminary results on toy and artificial networks, are promising. To the extent of our knowledge, a dynamic approach that includes link and node information, for soft overlapping community detection, has not been proposed before.