Detect Local Community based on Core Node using Deep Feature Fusion

Abstract

Unlike global community detection, local community detection is to identify a cluster of nodes sharing similar feature information based on a given seed, which is of great significance for many real-world applications. The most popular strategies of local community detection involve either expanding local communities around seed nodes or dividing communities through subgraph clustering. However, the accuracy of many local community detection algorithms heavily relies on the quality of seed nodes. Only high-quality seed nodes can accurately detect local communities. At the same time, the inability to effectively obtain node attributes and structural information also leads to an increase in subgraph clustering error rates. In this paper, we propose a Local Community Detection based on a Core Node using deep feature fusion, named LCDCN. For the seed node, we first find the nearest node with greater significance and correlation as the core node, then construct a \(k\)-subgraph through a specific subgraph extractor based on the core node. Subsequently, two deep encoders are employed to encode and fuse the attribute and structure information of the subgraph, respectively.Finally, by optimizing the fused feature representation through a self-supervised optimization function, the local community is discovered. Extensive experiments on 10 real datasets and 4 synthetic datasets demonstrate that LCDCN outperforms its competitors in terms of performance.