Inferring tag co-occurrence relationship across heterogeneous social networks

Predicting the occurrence of links or interactions between objects in a network is a fundamental problem in network analysis. 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 this novel problem mentioned previously, we propose a novel three-step prediction approach. First, image-tag bins are generated by utilizing the TF-IDF like method, which help reduce the search space. And then, weight path-based topological features are systematically extracted from the network. At last, 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 Flickr networks.