Enhance Ambiguous Community Structure via Multi-strategy Community Related Link Prediction Method with Evolutionary Process

TL;DR

This paper introduces a novel community attribute-based link prediction method called HAP, which enhances ambiguous community structures in real-world networks by adding links through an evolutionary process, improving community detection accuracy.

Contribution

The paper proposes a new link prediction strategy HAP that considers community attributes and uses an automatic evolution process for community enhancement in incomplete networks.

Findings

HAP outperforms baseline link prediction methods on real-world datasets.

Community structures become clearer after applying the enhancement algorithm.

The community evolution process aligns with expected theoretical patterns.

Abstract

Most real-world networks suffer from incompleteness or incorrectness, which is an inherent attribute to real-world datasets. As a consequence, those downstream machine learning tasks in complex network like community detection methods may yield less satisfactory results, i.e., a proper preprocessing measure is required here. To address this issue, in this paper, we design a new community attribute based link prediction strategy HAP and propose a two-step community enhancement algorithm with automatic evolution process based on HAP. This paper aims at providing a community enhancement measure through adding links to clarify ambiguous community structures. The HAP method takes the neighbourhood uncertainty and Shannon entropy to identify boundary nodes, and establishes links by considering the nodes' community attributes and community size at the same time. The experimental results on twelve real-world datasets with ground truth community indicate that the proposed link prediction method outperforms other baseline methods and the enhancement of community follows the expected evolution process.