A Network Embedding-Enhanced NMF Method for Finding Communities in Attributed Networks

Abstract

Community detection is an extremely important task for complex network analysis. There still remains a challenge of how to improve the performance of community detection in real-world scenario. Some researchers think that the content in networks is helpful to identify communities, and also focus on combining network topology with node contents, alongside the eradication of inauspicious performance. Furthermore, network topology is often sparse, which is reflected in the lack of capability to represent communities. To address the above problems, this study identifies a novel non-negative matrix factorization method which both employs network embedding to enhance the representation power of network topology for communities and also integrates network topology and node contents for further raising the quality of community detection. Furthermore, we then obtain the parameters of the model for finding communities which is based on model inference. Alongside both synthetic and real-world networks with ground-truths, we compare the new method with the state-of-the-art methods. Experimental results show that the new method obtains significant improvement for community detection both by incorporating node contents and by enhancing network topology.

Keywords

• Community detection
• network embedding
• node contents
• non-negative matrix factorization