Local search for constrained graph clustering in biological networks

Semi-supervised or constrained graph clustering incorporates prior information in order to improve clustering results. Pairwise constraints are often utilized to guide the clustering process. This work addresses a constrained graph clustering problem in biological networks where (1) subgraph connectivity constraints are strictly required to be satisfied and (2) clustering quality is assessed with respect to pairwise constraint violations. Existing constrained graph clustering methods often fail to fully satisfy connectivity constraints. This paper presents an iterated local search algorithm which aims to find the clustering with the highest quality in a short computing time. Experiments demonstrate how the proposed solutions are of good quality, often being optimal. Additionally, the proposed method significantly outperforms an existing branch-and-cut algorithm in terms of computational runtime and produces competitive results with regard to other local search techniques and graph clustering algorithms. Furthermore, a multilevel algorithm for clustering is designed to handle large-scale graphs. The performance of the overall scheme for a variety of coarsening methods from the literature is studied on a large number of biological networks exceeding 10,000 genes.

Automated summary

This paper presents an iterated local search algorithm for constrained graph clustering in biological networks, aiming to find the clustering with the highest quality in a short computing time. Experiments demonstrate the proposed algorithm's good quality and efficiency, outperforming existing branch-and-cut algorithms and producing competitive results with other local search techniques and graph clustering algorithms. Additionally, a multilevel algorithm for clustering is designed to handle large-scale graphs, achieving promising results on various coarsening methods applied to biological networks exceeding 10,000 genes.