Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Multiobjective clustering with automatic k-determination for large-scale data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic clustering of social networks using random walks
Computational Statistics & Data Analysis
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
FRINGE: a new approach to the detection of overlapping communities in graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
A link clustering based overlapping community detection algorithm
Data & Knowledge Engineering
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Extracting and understanding community structure in complex networks is one of the most intensively investigated problems in recent years. In this paper we propose a genetic based approach to discover overlapping communities. The algorithm optimizes a fitness function able to identify densely connected groups of nodes by employing it on the line graph corresponding to the graph modelling the network. The method generates a division of the network in a number of groups in an unsupervised way. This number is automatically determined by the optimal value of the fitness function. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.