Algorithms for clustering data
Algorithms for clustering data
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Greedy Search Approach to Co-clustering Sparse Binary Matrices
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
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
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
A non-dominated neighbor immune algorithm for community detection in networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Collaborative community detection in complex networks
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
On selection of objective functions in multi-objective community detection
Proceedings of the 20th ACM international conference on Information and knowledge management
Multi-objective community detection in complex networks
Applied Soft Computing
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Boosting the detection of modular community structure with genetic algorithms and local search
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Adaptable swarm intelligence framework
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A multiobjective hybrid evolutionary algorithm for clustering in social networks
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A posteriori approach for community detection
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Semi supervised clustering: a pareto approach
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Community detection in social and biological networks using differential evolution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Identification of multi-resolution network structures with multi-objective immune algorithm
Applied Soft Computing
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Interaction-based group identity detection via reinforcement learning and artificial evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Community Detection in Complex Networks: Multi-objective Enhanced Firefly Algorithm
Knowledge-Based Systems
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
The problem of community structure detection in complex networks has been intensively investigated in recent years. In this paper we propose a genetic based approach to discover communities in social networks. The algorithm optimizes a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups. The method is efficient because the variation operators are modified to take into consideration only the actual correlations among the nodes, thus sensibly reducing the research space of possible solutions. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.