Local search for constrained graph clustering in biological networks

• Published in: Computers & operations research Vol. 132; p. 105299
• Main Authors: Tran, Duy Hoang, Babaki, Behrouz, Van Daele, Dries, Leyman, Pieter, De Causmaecker, Patrick
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.08.2021; Pergamon Press Inc
• Subjects: Algorithms; AMG-based coarsening; Clustering; Computing time; Constrained graph clustering; Constraints; Graph theory; Iterated local search; Kernighan-Lin algorithm; Multilevel clustering; Networks; Operations research; Quality assessment; Search algorithms; Kernighan-Lin algorithm; Iterated local search; AMG-based coarsening; Constrained graph clustering; Multilevel clustering
• ISSN: 0305-0548; 0305-0548
• DOI: 10.1016/j.cor.2021.105299

•A constrained graph clustering problem with applications in cancer research.•Exact method is capable of finding optimal solutions while satisfying all constraints.•Iterated local search significantly outperforms in terms of computational runtime.•Multilevel approach is proposed for clustering large graphs. 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.