Robust Overlapping Community Detection in Complex Networks With Graph Convolutional Networks and Fuzzy C-Means

Abstract

Community detection is an important task in complex network analysis. A community is a set of cohesive vertices that have more connections within the set than outside. In many real Complex Networks (CNs), these communities naturally overlap, meaning an individual node can belong to more than one community. This overlapping structure is crucial for many real applications, such as social influence detection, cyberattack detection, and recommendation systems. Existing methods often struggle to capture both network topology and node features, leading to suboptimal overlapping community detection. In this paper, we propose an efficient method called GCNFCM, which utilizes Graph Convolutional Networks (GCNs), Fuzzy C-means (FCM), and the modularity Q algorithm for overlapping community detection. The key idea is to achieve robust feature learning for nodes and then identify the best structure for overlapping community detection. GCNFCM extracts node embeddings from CNs, considering both topology and attributes through a dual-decoder design (inner product and GCN), while FCM is employed for optimal overlapping community detection. Furthermore, FCM is guided by the modularity Q algorithm for accurate community identification without requiring prior knowledge of the community count. Experimental results on ten real-world CNs of varying sizes demonstrate that our proposed method outperforms other state-of-theart overlapping community detection methods in terms of producing cohesive communities and identifying ground-truth communities. Additionally, the results indicate that the developed method effectively identifies good overlapping communities in real-world networks. INDEX TERMSGraph Convolutional Networks, Fuzzy C-means, Complex networks. Overlapping Structure, Community detection. I. INTRODUCTIONNowadays, with the rapid expansion of information technology, things in the real world are more connected than ever because and usually represented as complex networks [1]. Complex Networks (CN) are distinguished by intricate interconnections, which serve as a powerful analytical tool for understanding a variety of interconnected systems in the real world. These systems encompass diverse domains such as the internet, biological neural networks, transportation systems, and social networks [1,2]. Complex networks like social media, protein interactions, and city routes are difficult to understand because they have complicated structures and keep changing. In real-world networks, connected groups are called "communities", which share common things. Identifying these communities is crucial for understanding network structures, and is valuable in different areas like biology, sociology, and computer science. For example, in computer science, finding