Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
Extraction and classification of dense communities in the web
Proceedings of the 16th international conference on World Wide Web
Exploring Local Community Structures in Large Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithm for improving graph partitions
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
A local algorithm for finding dense subgraphs
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Graph partitioning using single commodity flows
Journal of the ACM (JACM)
Identification of Web Communities through Link Based Approaches
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
Local Community Identification in Social Networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
A local algorithm for finding dense subgraphs
ACM Transactions on Algorithms (TALG)
C&C: an effective algorithm for extracting web community cores
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
Computer Science Review
| Hi-index | 0.00 |
To identify global community structure in networks is a great challenge that requires complete information of graphs, which is not feasible for some large networks, e.g. the World Wide Web. Recently, local algorithms have been proposed to extract communities in nearly linear time, which just require a small part of the graphs. However, their results, largely depending on the starting vertex, are not stable. In this paper, we propose a local modularity method for extracting local communities from local cores instead of random vertices. This approach firstly extracts a large enough local core with a heuristic strategy. Then, it detects the corresponding local community by optimizing local modularity, and finally removes outliers based on introversion. Experiment results indicate that, compared with previous algorithms, our method can extract stable meaningful communities with higher quality.