Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting the fuzzy clusters of complex networks
Pattern Recognition
An extended validity index for identifying community structure in networks
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Finding and evaluating fuzzy clusters in networks
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
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Detecting the community structure exhibited by real networks is a crucial step toward an understanding of complex systems beyond the local organization of their constituents. Many algorithms proposed so far, especially the group of methods in the k-means formulation, can lead to a high degree of efficiency and accuracy. Here we test three k-means methods, based on optimal prediction, diffusion distance and dissimilarity index, respectively, on two artificial networks, including the widely known ad hoc network with same community size and a recently introduced LFR benchmark graphs with heterogeneous distributions of degree and community size. All of them display an excellent performance, with the additional advantage of low computational complexity, which enables the analysis of large systems. Moreover, successful applications to several real world networks confirm the capability of the methods.