Name
Abstract | ||
|---|---|---|
Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1963192.1963260 | WWW (Companion Volume) |
Keywords | Field | DocType |
novel method,parameter estimation,performance optimization,adjusted rand index value,fast clustering,complex network feature,clustering algorithm,main challenge,real-world network,complex network,indexation,complex networks,graph clustering | Fuzzy clustering,Data mining,Computer science,Rand index,Hierarchical network model,Complex network,Artificial intelligence,Clustering coefficient,Cluster analysis,World Wide Web,Correlation clustering,Constrained clustering,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.41 | 2 |
Authors | ||
5 | ||