Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. Current approaches, however, have difficulty splitting two clusters that are densely connected by one or more hub vertices. Further, traditional methods are less able to deal with very confused structures. An extended modularity conception based on similarity for graph partitioning is proposed. Experimental results demonstrate that the proposed modularity largely identifies hubs and obtains higher accuracy than competing methods. In addition, we show that this definition can be incorporated with Newman's fast agglomerative algorithms to detect communities in very large networks.