Improved community structure detection using a modified fine tuning strategy

TL;DR

This paper introduces a modified fine-tuning strategy for community detection in complex networks that removes bias and improves the accuracy of existing algorithms without increasing computational complexity.

Contribution

It proposes a new step added to existing algorithms that eliminates bias caused by recursive bisection, enhancing their ability to find optimal community partitions.

Findings

The modified algorithm reduces bias in community size detection.

It achieves superior results on real-world networks compared to existing methods.

The approach maintains computational efficiency while improving accuracy.

Abstract

The community structure of a complex network can be determined by finding the partitioning of its nodes that maximizes modularity. Many of the proposed algorithms for doing this work by recursively bisecting the network. We show that this unduely constrains their results, leading to a bias in the size of the communities they find and limiting their effectivness. To solve this problem, we propose adding a step to the existing algorithms that does not increase the order of their computational complexity. We show that, if this step is combined with a commonly used method, the identified constraint and resulting bias are removed, and its ability to find the optimal partitioning is improved. The effectiveness of this combined algorithm is also demonstrated by using it on real-world example networks. For a number of these examples, it achieves the best results of any known algorithm.