Skip to Main content Skip to Navigation
Journal articles

OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks

Abstract : Community structure is one of the most prominent features of complex networks. Community structure detection is of great importance to provide insights into the network structure and functionalities. Most proposals focus on static networks. However, finding communities in a dynamic network is even more challenging, especially when communities overlap with each other. In this article , we present an online algorithm, called OLCPM, based on clique percolation and label propagation methods. OLCPM can detect overlapping communities and works on temporal networks with a fine granularity. By locally updating the community structure, OLCPM delivers significant improvement in running time compared with previous clique percolation techniques. The experimental results on both synthetic and real-world networks illustrate the effectiveness of the method.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01761341
Contributor : Remy Cazabet <>
Submitted on : Tuesday, April 10, 2018 - 12:53:40 PM
Last modification on : Thursday, November 21, 2019 - 2:01:40 AM

Files

elsarticle-template.pdf
Files produced by the author(s)

Identifiers

Citation

Souâad Boudebza, Rémy Cazabet, Faiçal Azouaou, Omar Nouali. OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks. Computer Communications, Elsevier, In press, ⟨10.1016/j.comcom.2018.04.003⟩. ⟨hal-01761341⟩

Share

Metrics

Record views

475

Files downloads

497