Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
The landscape adaptive particle swarm optimizer
Applied Soft Computing
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Community detection in social networks with genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
| Hi-index | 0.00 |
Community detection consists on a partitioning networks technique into clusters (communities) with weak coupling (external connectivity) and high cohesion (internal connectivity). In order to measure the performance of the clustering, the network modularity is largely used, a metric that presents the cohesion and the coupling of communities. In this paper, a global and bi-objective function is proposed to evaluate community detection. This function combines modularity (based on structure and edges weights) and the inter-classes inertia (based on nodes weights). Then, we rely on a computational optimization technique i.e. Particle Swarm Optimization to maximize this bi-objective quality. Finally, a case study evaluates the proposed solution and illustrates practical uses.