Multi-scale Community Detection using Stability Optimisation within Greedy Algorithms
Abstract
Many real systems can be represented as networks whose analysis can be very informative regarding the original system's organisation. In the past decade community detection received a lot of attention and is now an active field of research. Recently stability was introduced as a new measure for partition quality. This work investigates stability as an optimisation criterion that exploits a Markov process view of networks to enable multi-scale community detection. Several heuristics and variations of an algorithm optimising stability are presented as well as an application to overlapping communities. Experiments show that the method enables accurate multi-scale network analysis.
Cite
@article{arxiv.1201.3307,
title = {Multi-scale Community Detection using Stability Optimisation within Greedy Algorithms},
author = {Erwan Le Martelot and Chris Hankin},
journal= {arXiv preprint arXiv:1201.3307},
year = {2015}
}
Comments
This paper is an extension of the paper named "Multi-scale Community Detection using Stability as Optimisation Criterion in a Greedy Algorithm" by the same authors published in Proc. of the 2011 Int. Conf. on Knowledge Discovery and Information Retrieval (KDIR 2011), SciTePress, 2011, 216-225