Community Detection of Complex Network Based on Graph Convolution Iterative Algorithm

TL;DR

This paper introduces a novel community detection method for complex networks using a graph convolution iterative algorithm, improving accuracy and efficiency over existing methods.

Contribution

It proposes a new community detection approach combining graph convolution and iterative algorithms, with a novel community partitioning method based on convolutional node attributes.

Findings

Effective in multiple random networks

Outperforms baseline methods in accuracy

Validated on real-world networks

Abstract

Community detection can reveal the underlying structure and patterns of complex networks, identify sets of nodes with specific functions or similar characteristics, and study the evolution process and development trends of networks. Despite the myriad community detection methods that have been proposed, researchers continue to strive for ways to enhance the accuracy and efficiency of these methods. Graph convolutional neural networks can continuously aggregate the features of multiple neighboring nodes and have become an important tool in many fields. In view of this, this paper proposes a community detection method for complex networks based on graph convolution iteration algorithm. Firstly, the candidate community centers are determined by random sampling and the node attribute matrix is obtained based on the distances of nodes to community centers. Next, the graph convolution operation is implemented to obtain the convolutional node attribute matrix. Then, community partitioning method according to the convolutional node attribute matrix is presented and the effectiveness of community partitioning is measured through modularity. The method proposed in this paper is applied into to multiple random and real-world networks. The comparison results with some baseline methods demonstrate its effectiveness.