Multiview Community Discovery Algorithm via Nonnegative Factorization Matrix in Heterogeneous Networks

With the rapid development of the Internet and communication technologies, a large number of multimode or multidimensional networks widely emerge in real-world applications. Traditional community detection methods usually focus on homogeneous networks and simply treat different modes of nodes and connections in the same way, thus ignoring the inherent complexity and diversity of heterogeneous networks. It is challenging to effectively integrate the multiple modes of network information to discover the hidden community structure underlying heterogeneous interactions. In our work, a joint nonnegative matrix factorization (Joint-NMF) algorithm is proposed to discover the complex structure in heterogeneous networks. Our method transforms the heterogeneous dataset into a series of bipartite graphs correlated. Taking inspiration from the multiview method, we extend the semisupervised learning from single graph to several bipartite graphs with multiple views. In this way, it provides mutual information between different bipartite graphs to realize the collaborative learning of different classifiers, thus comprehensively considers the internal structure of all bipartite graphs, and makes all the classifiers tend to reach a consensus on the clustering results of the target-mode nodes. The experimental results show that Joint-NMF algorithm is efficient and well-behaved in real-world heterogeneous networks and can better explore the community structure of multimode nodes in heterogeneous networks.