A traffic-based evolutionary algorithm for network clustering
We propose a network clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. The algorithm can be successfully applied, rather than topology-based algorithms, to uncover relationships and detect communities whenever two nodes have a relationship though not being directly connected. We compare our algorithm to other clustering algorithms, both evolutionary and non evolutionary. Our algorithm outperforms all the other algorithms on all the real world datasets and outperforms all in the case of synthetic traffic matrices, excepting Newman's for the single case of a network of 75 nodes.
clustering traffic matrices genetic algorithms