Integrating topology and content equally in non-negative matrix factorization for community detection

Abstract

Non-negative matrix factorization (NMF) is widely used in analyzing the community structure of complex networks. However, most of the current related efforts only focus on the network structural topology, while neglecting the node content. Although there have been efforts to integrate topology and content, inefficient strategies balancing their contributions can also lead to unsatisfactory results. In this work, we treat topology and content equally and propose two novel NMF-based methods to finely balance topology and content to improve the performance of community detection. We first propose a double encoder–decoder-based NMF (DEDNMF) method that employs two NMF-based encoder–decoders to integrate topology and content adaptively. Experimental results show that DEDNMF outperforms the baseline methods with average improvements of 50.69% and 46.93% in terms of normalized mutual information (NMI) and adjusted rand index (ARI) on all experimental networks, respectively. Moreover, we propose a double-decoder-based NMF (DDNMF) method that uses only two decoders for both topology and content, imposing sparsity constraints on NMF. Experimental results show that DDNMF outperforms the baseline methods with average improvements of 72.11% and 77.00% in terms of NMI and ARI on all experimental networks. Experimental results also show that the proposed two methods have good convergence and robustness.