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
IEEE Transactions on Knowledge and Data Engineering
A Scalable Multilevel Algorithm for Graph Clustering and Community Structure Detection
Algorithms and Models for the Web-Graph
Computer Science Review
Visual mining of epidemic networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Modularity-driven clustering of dynamic graphs
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
A convex formulation of modularity maximization for community detection
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Parallel community detection for massive graphs
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
On the complexity of Newman's community finding approach for biological and social networks
Journal of Computer and System Sciences
A memetic algorithm for community detection in complex networks
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Graph-Based approaches to clustering network-constrained trajectory data
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Circle based community detection
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
Community detection by modularity maximization using GRASP with path relinking
Computers and Operations Research
Improving heuristics for network modularity maximization using an exact algorithm
Discrete Applied Mathematics
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Modularity is a widely used quality measure for graph clusterings. Its exact maximization is prohibitively expensive for large graphs. Popular heuristics progressively merge clusters starting from singletons (coarsening), and optionally improve the resulting clustering by moving vertices between clusters (refinement). This paper experimentally compares existing and new heuristics of this type with respect to their effectiveness (achieved modularity) and runtime. For coarsening, it turns out that the most widely used criterion for merging clusters (modularity increase) is outperformed by other simple criteria, and that a recent multi-step algorithm is no improvement over simple single-step coarsening for these criteria. For refinement, a new multi-level algorithm produces significantly better clusterings than conventional single-level algorithms. A comparison with published benchmark results and algorithm implementations shows that combinations of coarsening and multi-level refinement are competitive with the best algorithms in the literature.