Community Detection in Complex Networks using Link Prediction

TL;DR

This paper introduces a new community detection algorithm that incorporates link prediction to enhance accuracy, demonstrating that link prediction can effectively improve community partitioning in complex networks.

Contribution

The paper presents a novel community detection method that integrates two new link prediction indices, improving partition accuracy over traditional approaches.

Findings

Link prediction improves community detection precision.

The proposed algorithm outperforms baseline methods.

Experimental results validate the effectiveness of the approach.

Abstract

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baseline. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.