Detecting overlapping structures via network-based competitive learning

In this paper, we present a method for determining overlapping cluster structures in the network using a particle competition model. Specifically, several particles walk in the network and compete with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The overlapping nodes in the input data are uncovered by using the domination level information generated by the competition process itself. In this way, the detection procedure is already embedded in the model, which in turn has low computational complexity. Computer simulations reveal that this overlapping index works well in real-world data sets. Finally, an application on handwritten data clustering is provided and high clustering accuracies are obtained.