Authors:
Erwan Le Martelot
and
Chris Hankin
Affiliation:
Imperial College London, United Kingdom
Keyword(s):
Community detection, Multi-scale, Multi-resolution, Network analysis, Stability, Modularity, Network partition, Greedy optimisation, Markov process.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Whether biological, social or technical, many real systems are represented as networks whose structure can be very informative regarding the original system’s organisation. In this respect the field of community detection has received a lot of attention in the past decade. Most of the approaches rely on the notion of modularity to assess the quality of a partition and use this measure as an optimisation criterion. Recently stability was introduced as a new partition quality measure encompassing former partition quality measures such as modularity. The work presented here assesses stability as an optimisation criterion in a greedy approach similar to modularity optimisation techniques and enables multi-scale analysis using Markov time as resolution parameter. The method is validated and compared with other popular approaches against synthetic and various real data networks and the results show that the method enables accurate multi-scale network analysis.