Name
Abstract | ||
|---|---|---|
The hidden knowledge in the information network has attracted a large number of researchers from different subjects such as sociology, physics and computer science. Community discovery has great significance for the analysis of information network structure, the understanding of its function, the discovery of its hidden patterns, and the predication of its behavior. In the practical life, people tend to analyze the information network with a heuristic method, that is, analyze the partial structure which meets the specific needs abstracted from the huge amounts of relational data. For this case, a method of community discovery based on seeds expansion is put forward in this paper. The node that should be paid special attention to in the information network is called the seed node, and then nodes with high similarity with the seed node are added through the iterative way. Accepting the idea of clustering algorithm, this method can not only find its community according to the customization node, but also find the outlier nodes of the community. Experiments on the public test set and data set of Sina micro-blog have demonstrated the effectiveness of the method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CGC.2012.69 | CGC |
Keywords | Field | DocType |
pattern clustering,physics,clustering algorithm,social network,local community detection,sociology,information network,relational data,hidden knowledge,seeds expansion,community discovery,partial structure,community outlier node,hidden pattern,information network structure,seed node,sina microblog,computer science,social networking (online),outlier node,heuristic method,customization node | Local community,Data mining,Heuristic,Social network,Relational database,Computer science,Outlier,Cluster analysis,Test set,Personalization | Conference |
ISBN | Citations | PageRank |
978-1-4673-3027-5 | 2 | 0.41 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingying Xu | 1 | 10 | 3.27 |
| Zheng Liang | 2 | 27 | 8.81 |
| Yan Jia | 3 | 56 | 10.52 |
| Bin Zhou | 4 | 341 | 30.99 |
| Yi Han | 5 | 93 | 8.94 |