The efficiency of community detection by most similar node pairs

TL;DR

This paper evaluates methods for community detection in complex systems using most similar node pairs, demonstrating that selecting nodes with maximum similarity effectively reveals community structures.

Contribution

It introduces five strategies for selecting most similar node pairs and compares their efficiency in community detection within a complex system.

Findings

Maximum similarity strategy outperforms others in community detection

Random selection results in small-world local communities with no internal order

Normalized mutual information effectively measures detection efficiency

Abstract

Community analysis is an important way to ascertain whether or not a complex system consists of sub-structures with different properties. In this paper, we give a two level community structure analysis for the SSCI journal system by most similar co-citation pattern. Five different strategies for the selection of most similar node (journal) pairs are introduced. The efficiency is checked by the normalized mutual information technique. Statistical properties and comparisons of the community results show that both of the two level detection could give instructional information for the community structure of complex systems. Further comparisons of the five strategies indicates that, the most efficient strategy is to assign nodes with maximum similarity into the same community whether the similarity information is complete or not, while random selection generates small world local community with no inside order. These results give valuable indication for efficient community detection by most similar node pairs.