Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Silk from a sow's ear: extracting usable structures from the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
Hi-index | 0.00 |
The hierarchical clustering methods based on vertex similarity can be employed for community discovery. Vertex similarity metric is the most important part of these methods. However, the existing metrics do not perform well compared with the state-of-the-art algorithms. In this paper, we propose a new vertex similarity metric based on distance neighbor model, called Distance Neighbor Ratio Metric (DNRM), for community discovery. DNRM considers both distance and nearby edge density which are essential measures in community structure. Compared with the existing metrics of vertex similarity, DNRM outperforms substantially in community discovery quality and the computing time. The experiments are designed rigorously involving both well-known social networks in real world and computer generated networks.