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
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Agglomerative genetic algorithm for clustering in social networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A particle-and-density based evolutionary clustering method for dynamic networks
Proceedings of the VLDB Endowment
SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Community detection in incomplete information networks
Proceedings of the 21st international conference on World Wide Web
Boosting the detection of modular community structure with genetic algorithms and local search
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Graph partitioning, or network clustering, is an essential research problem in many areas. 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. In this paper we propose a novel similarity-based definition of the quality of a partitioning of a graph. Through theoretical analysis and experimental results we demonstrate that the proposed definition largely overcomes the "hub" problem and outperforms existing approaches on complicated graphs. In addition, we show that this definition can be used with fast agglomerative algorithms to find communities in very large networks.