@inproceedings{98c8fd0f1ad947c4ba14eead0a86e886,
title = "A SAT-based framework for overlapping community detection in networks",
abstract = "In this paper, we propose a new approach to detect overlapping communities in large complex networks. We first introduce a parametrized notion of a community, called k -linked community, allowing us to characterize node/edge centered k-linked community with bounded diameter. Such community admits a node or an edge with a distance at most k/2 from any other node of that community. Next, we show how the problem of detecting node/edge centered k-linked overlapping communities can be expressed as a Partial Max-SAT optimization problem. Then, we propose a post-processing strategy to limit the overlaps between communities. An extensive experimental evaluation on real-world networks shows that our approach outperforms several popular algorithms in detecting relevant communities.",
author = "Said Jabbour and Nizar Mhadhbi and Badran Raddaoui and Lakhdar Sais",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 ; Conference date: 23-05-2017 Through 26-05-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-57529-2\_61",
language = "English",
isbn = "9783319575285",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "786--798",
editor = "Longbing Cao and Kyuseok Shim and Jae-Gil Lee and Jinho Kim and Yang-Sae Moon and Xuemin Lin",
booktitle = "Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings",
}