Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis
| dc.contributor.author | ZHAO, Feng | en_US |
| dc.contributor.author | TUNG, Anthony K. H. | en_US |
| dc.date.accessioned | 2012-04-03T09:41:45Z | en_US |
| dc.date.accessioned | 2017-01-23T07:00:07Z | |
| dc.date.available | 2012-04-03T09:41:45Z | en_US |
| dc.date.available | 2017-01-23T07:00:07Z | |
| dc.date.issued | 2012-04-03T09:41:45Z | en_US |
| dc.description.abstract | Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, e®ectively storing large scale social network and e±ciently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O e±cient approach to discover cohesive sub- graphs. Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then visualizes the sub-graph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a uni¯ed view. | en_US |
| dc.format.extent | 645815 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/3592 | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TRB4/12 | en_US |
| dc.title | Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis | en_US |
| dc.type | Technical Report | en_US |