Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
IEEE Transactions on Knowledge and Data Engineering
Multi-level Algorithms for Modularity Clustering
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Online community detection for large complex networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Complex networks pervade in diverse areas ranging from the natural world to the engineered world and from traditional application domains to new and emerging domains, including web-based social networks. Of crucial importance to the understanding of many network phenomena, dynamics and functions is the study of network structural properties. One important type of network structure is known as community structure which refers to the existence of communities that are tightly knit local groups with relatively dense connections among their members. Community detection is the problem of detecting these communities automatically. In this paper, based on the modularity measure proposed previously for community detection, we first propose a reformulation of an optimization problem for the 2-partition problem. Based on this new formulation, we can extend it naturally for tackling the general k-partition problem directly without having to tackle multiple 2-partition subproblems like what other methods do. We then propose a convex relaxation scheme to give an iterative algorithm which solves a simple quadratic program in each iteration. We empirically compare our method with some related methods and find that our method is both scalable and competitive in performance via maintaining a good tradeoff between efficiency and quality.