Algorithms for graph partitioning on the planted partition model
Random Structures & Algorithms
Optimization with extremal dynamics
Complexity - Complex Adaptive systems: Part I
A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Hierarchical, Parameter-Free Community Discovery
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel community detection on large networks with propinquity dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient community detection using power graph analysis
Proceedings of the 9th workshop on Large-scale and distributed informational retrieval
On community detection in real-world networks and the importance of degree assortativity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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The community detection in networks is a prominent task in the graph data mining, because of the rapid emergence of the graph data; e.g., information networks or social networks. In this paper, we propose a new algorithm for detecting communities in networks. Our approach differs from others in the ability of constraining the size of communities being generated, a property important for a class of applications. In addition, the algorithm is greedy in nature and belongs to a small family of community detection algorithms with the pseudo-linear time complexity, making it applicable also to large networks. The algorithm is able to detect small-sized clusters independently of the network size. It can be viewed as complementary approach to methods optimizing modularity, which tend to increase the size of generated communities with the increase of the network size. Extensive evaluation of the algorithm on synthetic benchmark graphs for community detection showed that the proposed approach is very competitive with state-of-the-art methods, outperforming other approaches in some of the settings.