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

Autor:	Boudebza, Souâad, Cazabet, Rémy, Azouaou, Faiçal, Nouali, Omar
Rok vydání:	2018
Předmět:	Computer Science - Data Structures and Algorithms Computer Science - Social and Information Networks Physics - Physics and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: Journal of Computer Communications, In press
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1804.03842 Zobrazit plný text záznamu View this record from Arxiv