Name
Abstract | ||
|---|---|---|
Discovering the hidden community structure is a fundamental problem in network and graph analysis. Several approaches have been proposed to solve this challenging problem. Among them, detecting overlapping communities in a network is a usual way towards understanding the features of networks. In this paper, we propose a novel approach to identify overlapping communities in large complex networks. It makes an original use of a new community model, called k-clique-star, to discover densely connected structures in social interactions. We show that such model allows to ensure a minimum density on the discovered communities and overcomes some weaknesses of existing cohesive structures. Experimental results demonstrate the effectiveness and efficiency of our overlapping community model in a variety of real graphs. |
Year | Venue | Field |
|---|---|---|
2018 | IDA | Graph,Community structure,Social network,Computer science,Power graph analysis,Complex network,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saïd Jabbour | 1 | 5 | 2.46 |
| Nizar Mhadhbi | 2 | 0 | 2.70 |
| Badran Raddaoui | 3 | 93 | 15.31 |
| Lakhdar Sais | 4 | 859 | 65.57 |