Name
Title | ||
|---|---|---|
Using the clustering coefficient to guide a genetic-based communities finding algorithm |
Abstract | ||
|---|---|---|
Finding communities in networks is a hot topic in several research areas like social network, graph theory or sociology among others. This work considers the community finding problem as a clustering problem where an evolutionary approach can provide a new method to find overlapping and stable communities in a graph. We apply some clustering concepts to search for new solutions that use new simple fitness functions which combine network properties with the clustering coefficient of the graph. Finally, our approach has been applied to the Eurovision contest dataset, a well-known social-based data network, to show how communities can be found using our method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23878-9_20 | IDEAL |
Keywords | Field | DocType |
social network,network property,genetic-based community,new solution,new simple fitness function,graph theory,new method,clustering coefficient,clustering problem,well-known social-based data network,clustering concept | Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Correlation clustering,Computer science,Hierarchical clustering of networks,Constrained clustering,Artificial intelligence,Clustering coefficient,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
6936 | 0302-9743 | 9 |
PageRank | References | Authors |
0.66 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gema Bello Orgaz | 1 | 116 | 10.36 |
| Héctor Menéndez | 2 | 171 | 15.75 |
| David Camacho | 3 | 278 | 24.89 |