Communications of the ACM
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Bridging centrality: graph mining from element level to group level
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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In the era of information explosion, structured data emerge on a large scale. As a description of structured data, network has drawn attention of researchers in many subjects. Network clustering, as an essential part of this study area, focuses on detecting hidden sub-group using structural features of networks. Much previous research covers measuring network structure and discovering clusters. In this paper, a novel structural metric "Graph Distance" and an effective clustering algorithm GRACE are proposed. The graph distance integrates local density of clusters with global structural properties to reflect the actual network structure. The algorithm GRACE generalizes hierarchical and locality clustering methods and outperforms some existing methods. An empirical evaluation demonstrates the performance of our approach on both synthetic data and real world networks.