Article: ICEP and ILEP: two new approaches to identify community of complex biological network Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2026 Vol.30 No.3/4 pp.199 - 233 Abstract: Understanding the internal modular organisation of protein-protein interactions is crucial for deciphering molecular-level biological processes. Recognition of network communities enhances our comprehension of the biological origins of disease pathogenesis. This research introduces two innovative community detection algorithms, iterative credit-edge pruning (ICEP) and iterative load-based edge pruning (ILEP), designed to identify communities within complex biological networks. Our algorithms are evaluated using real-world data from the Omicron dataset, and their performance is compared with four established algorithms: Girvan-Newman, Louvain, Leiden, and the label propagation algorithm. Validation of the community structures is achieved through modularity. Among the techniques compared, our proposed method, ICEP, stands out with the highest modularity score of 0.885, outperforming all other approaches. The alternative method, ILEP, also achieves a notable modularity score of 0.698, surpassing the Girvan-Newman method. By implementing ICEP and ILEP, we gain profound insights into the structural organisation and interconnections within the Omicron virus. Inderscience Publishers - linking academia, business and industry through research

Title: ICEP and ILEP: two new approaches to identify community of complex biological network

Authors: Mamata Das; K. Selvakumar; P.J.A. Alphonse

Addresses: Department of Computer Applications, National Institute of Technology Tiruchirappalli, Tamil Nadu, India ' Department of Computer Applications, National Institute of Technology Tiruchirappalli, Tamil Nadu, India ' Department of Computer Applications, National Institute of Technology Tiruchirappalli, Tamil Nadu, India

Abstract: Understanding the internal modular organisation of protein-protein interactions is crucial for deciphering molecular-level biological processes. Recognition of network communities enhances our comprehension of the biological origins of disease pathogenesis. This research introduces two innovative community detection algorithms, iterative credit-edge pruning (ICEP) and iterative load-based edge pruning (ILEP), designed to identify communities within complex biological networks. Our algorithms are evaluated using real-world data from the Omicron dataset, and their performance is compared with four established algorithms: Girvan-Newman, Louvain, Leiden, and the label propagation algorithm. Validation of the community structures is achieved through modularity. Among the techniques compared, our proposed method, ICEP, stands out with the highest modularity score of 0.885, outperforming all other approaches. The alternative method, ILEP, also achieves a notable modularity score of 0.698, surpassing the Girvan-Newman method. By implementing ICEP and ILEP, we gain profound insights into the structural organisation and interconnections within the Omicron virus.

Keywords: protein interaction network; omicron; community detection; modularity; graphlet; centrality.

DOI: 10.1504/IJDMB.2026.154688

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.3/4, pp.199 - 233

Received: 13 Mar 2024
Accepted: 29 Aug 2024
Published online: 10 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: ICEP and ILEP: two new approaches to identify community of complex biological network

Keep up-to-date