Link Prediction Based On Local Structure And Node Information Along Local Paths

Link prediction aims at predicting the missing links or new links based on known topological or attribute information of networks, which is one of the most significant and challenging tasks in complex network analysis. Recently, many local similarity-based methods have been proposed and they performed well in most cases. However, most of these methods simultaneously ignore the contributions of the local structure information between endpoints and their common neighbors, as well as transmission abilities of different 3-hop paths. To address these issues, in this paper, we propose a novel link prediction method that aims at improving the prediction accuracy of the existing local similarity-based methods by integrating with local structure information and node degree information along 3-hop paths. Extensive experiments have been performed on nine real-world networks and the results demonstrate that our proposed method is superior to the existing state-of-the-art methods.