Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A multilevel algorithm for partitioning graphs
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Multi-level Algorithms for Modularity Clustering
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Multilevel local search algorithms for modularity clustering
Journal of Experimental Algorithmics (JEA)
Community detection in Social Media
Data Mining and Knowledge Discovery
On the complexity of Newman's community finding approach for biological and social networks
Journal of Computer and System Sciences
Improving heuristics for network modularity maximization using an exact algorithm
Discrete Applied Mathematics
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One of the most useful measures of cluster quality is the modularity of the partition, which measures the difference between the number of the edges joining vertices from the same cluster and the expected number of such edges in a random (unstructured) graph. In this paper we show that the problem of finding a partition maximizing the modularity of a given graph Gcan be reduced to a minimum weighted cut problem on a complete graph with the same vertices as G. We then show that the resulted minimum cut problem can be efficiently solved with existing software for graph partitioning and that our algorithm finds clusterings of a better quality and much faster than the existing clustering algorithms.