The clustering model and algorithm of PPI network based on propagating mechanism of artificial bee colony

Abstract

Recently there are a large number of clustering methods applied to discover modules in protein–protein interaction (PPI) networks. However, due to the small-world and scale-free properties of PPI networks, most of them do not work well. This paper proposed a novel artificial bee colony (ABC) clustering model based on propagating mechanism. The ABC model based on propagating mechanism consisted of three different functions of bees which were named after queen, drone, and brood. The queen was regarded as a cluster center, and the drones stood for the sorted nodes according to the descending order of the aggregation coefficient of edge connecting these nodes with the queen node. The queen mated with the drones in order to cluster PPI data. In the end, the brood which is well-developed would be regarded as the new queen and went on a new mating-flight until all the nodes had been visited. This model could automatically obtain the cluster number during the clustering procedure, and the time complexity was greatly reduced. The simulation experiments on MIPS dataset showed that it performed well in terms of several criteria such as precision, recall and running time.