Overlapping Clustered Graphs: Co-authorship Networks Visualization
SG '08 Proceedings of the 9th international symposium on Smart Graphics
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Identification of multi-resolution network structures with multi-objective immune algorithm
Applied Soft Computing
Optimal Clustering Selection on Hierarchical System Network
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
CRUDAW: a novel fuzzy technique for clustering records following user defined attribute weights
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Optimal local community detection in social networks based on density drop of subgraphs
Pattern Recognition Letters
| Hi-index | 0.10 |
In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.