A local random walk model for complex networks based on discriminative feature combinations

Abstract

Networks have become increasingly important to model many complex systems. This powerful representation has been employed in different tasks of artificial intelligence including machine learning, expert and intelligent systems. Link prediction, a branch of network pattern recognition, is the most fundamental and essential problem for complex network analysis. However, most existing link-prediction methods only consider a network’s topology structures, and in doing so, these methods miss the opportunity to use nodes’ attribute information. We present a combined approach here that uses nodes’ attribute information and topology structure to direct link prediction. First, we propose a discriminative feature combinations selection method. Specifically, we present a novel mathematics inference to detail discriminative feature combinations. Second, based on the selected feature combinations, we aggregate the network, and further compute each feature combination’s contributing degree to the link’s formation, called the strength of feature combination. Third, we apply discriminative feature combinations into a local random walk model; in particular, we compute and redistribute the random walk particle’s transfer probability in terms of each feature combination’s strength, which makes the transfer probability depend on feature combinations satisfied by each node’s edges. Finally, we predict links in complex networks based on the improved random walk model. Experimental results on real-life complex network datasets demonstrate that, compared to other baseline methods, using discriminative feature combinations and topology structures in tandem strengthens prediction performance remarkably.