@inproceedings{0e8b0fcda1084723b4273c650220bb92,
title = "An ant colony optimization method to detect communities in social networks",
abstract = "Community detection is an important task in social network analysis. It aims to partition the network into clusters so that interactions among members within a cluster are considerably more frequent than that across clusters. A typical instantiation is to maximize the modularity of clusters which is a NP-hard problem, and thus, heuristic and meta-heuristic algorithms are employed as approximation. We present a novel divisive algorithm based on ant colony optimization to detect hierarchical community structure by maximizing the modularity. Our algorithm splits the network into two local communities iteratively and incorporates both heuristic information and pheromone trails. Experimental results on a set of synthetic benchmarks and real-world networks verified that our algorithm is highly effective for hierarchical community structure detection.",
keywords = "Ant Colony Optimization, Community Detection, Network Clustering, Social Network Analysis",
author = "Javadi, {Saeed H.S.} and Shahram Khadivi and Shiri, {M. Ebrahim} and Jia Xu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 ; Conference date: 17-08-2014 Through 20-08-2014",
year = "2014",
month = oct,
day = "10",
doi = "10.1109/ASONAM.2014.6921583",
language = "English",
series = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "200--203",
editor = "Martin Ester and Guandong Xu and Xindong Wu",
booktitle = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
}