A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma

• Published in: PloS one Vol. 16; no. 8; p. e0255718
• Main Authors: Ehsan Pournoor, Zaynab Mousavian, Abbas Nowzari-Dalini, Ali Masoudi-Nejad
• Format: Journal Article
• Language: English
• Published: Public Library of Science (PLoS) 01.01.2021
• ISSN: 1932-6203
• DOI: 10.1371/journal.pone.0255718

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.