Paper Info
Title
Dynamic consensus community detection and combinatorial multi-armed bandit.
Abstract
Community detection and evolution has been largely studied in the last few years, especially for network systems that are inherently dynamic and undergo different types of changes in their structure and organization in communities. Because of the inherent uncertainty and dynamicity in such network systems, we argue that temporal community detection problems can profitably be solved under a particular class of multi-armed bandit problems, namely combinatorial multi-armed bandit (CMAB). More specifically, we propose a CMAB-based methodology for the novel problem of dynamic consensus community detection, i.e., to compute a single community structure that is designed to encompass the whole information available in the sequence of observed temporal snapshots of a network in order to be representative of the knowledge available from community structures at the different time steps. Unlike existing approaches, our key idea is to produce a dynamic consensus solution for a temporal network to have unique capability of embedding both long-term changes in the community formation and newly observed community structures.
Year
DOI
Venue
2019
10.1145/3341161.3342910
ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining Vancouver British Columbia Canada August, 2019
Keywords
Field
DocType
Community detection in temporal networks, Multi-armed bandit problems, Complex network models
Computer science,Multi-armed bandit,Artificial intelligence,Machine learning
Conference
ISSN
ISBN
Citations 
2473-9928
978-1-4503-6868-1
0
PageRank 
References 
Authors
0.34
13
2
Name
Order
Citations
PageRank
Domenico Mandaglio112.04
Andrea Tagarelli247552.29