Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Partitioning of Web graphs by community topology
WWW '05 Proceedings of the 14th international conference on World Wide Web
Analysis of communities of interest in data networks
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Parallel programming with object assemblies
Proceedings of the 24th ACM SIGPLAN conference on Object oriented programming systems languages and applications
Hierarchical role classification based on social behavior analysis
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
| Hi-index | 0.00 |
We present a new approach to community discovery. Community discovery usually partitions the graph into communities or clusters. Focused community discovery allows the searcher to specify start points of interest, and find the community of those points. Focused search allows for a much more scalable algorithm in which the time depends only on the size of the community, and not on the number of nodes in the graph, and so is scalable to arbitrarily large graphs. Furthermore, our algorithm is robust to imperfect data, such as extra or missing edges in the graph. We show the effectiveness of our algorithm using both synthetic graphs and on the real-life Livejournal friends graph, a publicly-available social network consisting of over two million users and 13 million edges.