On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Out-of-core coherent closed quasi-clique mining from large dense graph databases
ACM Transactions on Database Systems (TODS)
Fast detection of size-constrained communities in large networks
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Ant colony optimization with Markov random walk for community detection in graphs
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Community detection in Social Media
Data Mining and Knowledge Discovery
Aiding the detection of fake accounts in large scale social online services
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Finding maximal k-edge-connected subgraphs from a large graph
Proceedings of the 15th International Conference on Extending Database Technology
Parallel community detection for massive graphs
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
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
Distributed community detection in web-scale networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
Hierarchical parallel algorithm for modularity-based community detection using GPUs
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Scalable community detection in massive social networks using MapReduce
IBM Journal of Research and Development
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Graphs or networks can be used to model complex systems. Detecting community structures from large network data is a classic and challenging task. In this paper, we propose a novel community detection algorithm, which utilizes a dynamic process by contradicting the network topology and the topology-based propinquity, where the propinquity is a measure of the probability for a pair of nodes involved in a coherent community structure. Through several rounds of mutual reinforcement between topology and propinquity, the community structures are expected to naturally emerge. The overlapping vertices shared between communities can also be easily identified by an additional simple postprocessing. To achieve better efficiency, the propinquity is incrementally calculated. We implement the algorithm on a vertex-oriented bulk synchronous parallel(BSP) model so that the mining load can be distributed on thousands of machines. We obtained interesting experimental results on several real network data.