Detecting network communities via greedy expanding based on local superiority index

TL;DR

This paper introduces a new local index called LSI for identifying central nodes in network community detection, improving the efficiency and accuracy of greedy expansion algorithms compared to existing methods.

Contribution

The paper proposes the local superiority index (LSI) for central node detection, enhancing greedy community detection algorithms with better performance and community quality.

Findings

LSI outperforms degree-based indices in identifying central nodes.

The greedy algorithm using LSI surpasses classical methods in most networks.

Evaluation shows improved community detection accuracy with LSI.

Abstract

Community detection is a significant and challenging task in network science. Nowadays, plenty of attention has been paid on local methods for community detection. Greedy expanding is a popular and efficient class of local algorithms, which typically starts from some selected central nodes and expands those nodes to obtain provisional communities by optimizing a certain quality function. In this paper, we propose a novel index, called local superiority index (LSI), to identify central nodes. In the process of expansion, we apply the fitness function to estimate the quality of provisional communities and ensure that all provisional communities must be weak communities. Evaluation based on the normalized mutual information suggests: (1) LSI is superior to the global maximal degree index and the local maximal degree index on most considered networks; (2) The greedy algorithm based on LSI is better than the classical fast algorithm on most considered networks.