Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/1265
Title: | APAL: Adjacency Propagation Algorithm for overlapping community detection in biological networks | Authors: | Doluca, Osman Oguz, Kaya |
Keywords: | Overlapping communities Biological networks Graph generation Cliques Modules Map |
Publisher: | Elsevier Science Inc | Abstract: | We propose a novel method called Adjacency Propagation Algorithm (APAL) which considers the notion that the adjacent vertices are the best candidates for detecting overlapping communities in an undirected, unweighted, nontrivial graph. This is a compact algorithm with a single threshold parameter used to filter the detected communities according to their intraconnectivity property. In this study, APAL was tested rigorously using synthetic generators, such as the widely accepted LFR benchmark, as well as real data sets of yeast and human protein interactions networks. It was compared against the foremost algorithms in the field; the Clique Percolation Method (CPM), Community Overlap Propagation Algorithm (COPRA) and Neighbourhood-Inflated Seed Expansion (NISE). The results show that APAL outperforms its competitors for networks with increases in the number of memberships of the overlapping vertices. Such conditions are often found in biological networks, where a particular protein subunit may form part of several complexes. We believe that this shows the value of the implementation of APAL for protein interaction and other biological networks. (c) 2021 Elsevier Inc. All rights reserved. | URI: | https://doi.org/10.1016/j.ins.2021.08.031 https://hdl.handle.net/20.500.14365/1265 |
ISSN: | 0020-0255 1872-6291 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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