Link Prediction Based on Precision Optimization

Abstract

In complex networks, link prediction involves detecting both unknown links and links that may appear in the future. Recently, various approaches have been proposed to detect potential or future links in temporal social networks. To evaluate the performance of link prediction methods, precision are usually used to measure the accuracy of the predicting results. This paper proposes an algorithm based on the precision optimization. In the method, precision is treated as the objective function, and link prediction is transformed as an optimization problem. A group of topological features are defined for each ordered pair of nodes. Using those features as the attributes of the node pairs, link prediction can be treated as a binary classification where class label of each node pair is determined by whether there exists a directed link between the node pair. Then the binary classification problem can be solved by precision optimization. Empirical results show that our algorithm can achieve higher quality results of prediction than other algorithms.