Tag Co-occurrence Relationship Prediction in Heterogeneous Information Networks

In this work, we address a novel problem about tag co-occurrence relationship prediction across heterogeneous networks. Although tag co-occurrence has recently become a hot research topic, many studies mainly focus on how to produce the personalized recommendation leveraging the tag co-occurrence relationship and most of them are considered in a homogeneous network. So far, few studies pay attention to how to predict tag co-occurrence relationship across heterogeneous networks. In order to solve the aforementioned problem, we propose a novel two-step prediction approach. First, weight path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-occurrence relationships. Experiments are performed on real-world dataset, the Flickr network, with comprehensive measurements. Experimental results demonstrate that weight path-based heterogeneous topological features have substantial advantages over commonly used link prediction approaches in predicting co-occurrence relations in information networks.