Enhanced Community Structure Detection in Complex Networks with Partial Background Information

TL;DR

This paper introduces a semi-supervised learning framework for community detection in complex networks that leverages prior information to improve result interpretability and accuracy, validated on synthetic and real-world data.

Contribution

It presents a novel semi-supervised approach that effectively incorporates prior knowledge to enhance community detection and explainability.

Findings

Framework outperforms traditional methods on synthetic networks

Effective utilization of prior information improves detection accuracy

Validated on real-world networks with promising results

Abstract

Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable. By logical inference, the prior information is more fully utilized. The experiments on both the synthetic and the real-world networks confirm the effectiveness of the framework.