Link Prediction Based on Node Similarity
基于节点相似性的链接预测

Link prediction is an important issue in graph mining. It aimed at estimating the likelihood of the existence of links between nodes by the known network structure information. Currently, most link prediction algorithms based on node similarity consider only the individual characteristics of common neighbor nodes. We designed a new algorithm exploiting the interactions between common neighbors, namely Individual Attraction Index While maintaining low time complexity, this algorithm remarkably improved the accuracy of prediction. I}his paper proved well the best overall performance of this new algorithm by comparing three well-known node similarity algorithms on eight real networks with Individual Attraction Index.