On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Mapping the Gnutella Network: Macroscopic Properties of Large-Scale Peer-to-Peer Systems
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Computational & Mathematical Organization Theory
Trust-based agent community for collaborative recommendation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
On the structural properties of massive telecom call graphs: findings and implications
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Graph clustering with network structure indices
Proceedings of the 24th international conference on Machine learning
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Graph Clustering Via a Discrete Uncoupling Process
SIAM Journal on Matrix Analysis and Applications
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
| Hi-index | 0.00 |
The community structure is one of the most important patterns in network. Since finding the communities in the network can significantly improve our understanding of the complex relations, lots of work has been done in recent years. Yet it still lies vacant on the exact definition and practical algorithms for community detection. This paper proposes a novel definition for community which overcomes the drawbacks of existing methods. With the new definition, efficient community detection algorithms are developed, which take advantage of additive topological and other constrains to discover communities in arbitrary shape based on the feedback. The algorithm has a linear run time with the size of graph. Experimental results demonstrate that the community definition in this paper is effective and the algorithm is scalable for large graphs.