Enhance Ambiguous Community Structure via Multi-strategy Community Related Link Prediction Method with Evolutionary Process
This work addresses the issue of improving community detection accuracy in complex networks, but it is incremental as it builds on existing link prediction and community enhancement techniques.
The paper tackles the problem of ambiguous community structures in incomplete real-world networks by proposing a new link prediction method (HAP) and a two-step community enhancement algorithm, which outperforms baseline methods on twelve datasets.
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.