Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Discovering community-oriented roles of nodes in a social network
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Computer Science Review
RoClust: Role discovery for graph clustering
Web Intelligence and Agent Systems
| Hi-index | 0.00 |
Graph clustering is an important task of discovering the underlying structure in a network. Well-known methods such as the normalized cut and modularity-based methods are developed in the past decades. These methods may be called non-overlapping because they assume that a vertex belongs to one community. On the other hand, overlapping methods such as CPM, which assume that a vertex may belong to more than one community, have been drawing attention as the assumption fits the reality. We believe that existing overlapping methods are overly simple for a vertex located at the border of a community. That is, they lack careful consideration on the edges that link the vertex to its neighbors belonging to different communities. Thus, we propose a new graph clustering method, named RoClust, which uses three different kinds of roles, each of which represents a different kind of vertices that connect communities. Experimental results show that our method outperforms state-of-the-art methods of graph clustering.