Title
Triangle-Driven Community Detection in Large Graphs Using Propositional Satisfiability
Abstract
Discovering the latent community structure is crucial to understanding the features of networks. Several approaches have been proposed to solve this challenging problem using different measures or data structures. Among them, detecting overlapping communities in a network is an usual way towards network structure discovery. It presents nice algorithmic issues, and plays an important role in complex network analysis. In this paper, we propose a new approach to detect overlapping communities in large complex networks. First, we introduce a novel subgraph concept based on triangles to capture the cohesion in social interactions, and propose an efficient approach to discover clusters in networks. Next, we show how the problem of detecting overlapping communities can be expressed as a Partial Max-SAT optimization problem. Our comprehensive experimental evaluation on publicly available real-life networks with ground-truth communities demonstrates the effectiveness and efficiency of our proposed method.
Year
DOI
Venue
2018
10.1109/AINA.2018.00072
2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)
Keywords
Field
DocType
Social Networks,Community Detection,Propositional Satisfiability
Cohesion (chemistry),Data structure,Graph,Community structure,Computer science,Satisfiability,Theoretical computer science,Complex network,Cluster analysis,Optimization problem,Distributed computing
Conference
ISSN
ISBN
Citations 
1550-445X
978-1-5386-2196-7
0
PageRank 
References 
Authors
0.34
21
4
Name
Order
Citations
PageRank
Saïd Jabbour1355.00
Nizar Mhadhbi202.70
Badran Raddaoui39315.31
Lakhdar Sais485965.57