Link Prediction Based on Weight Assignments in Complex Networks

Abstract

Link prediction is usually studied in unweighted networks, while ignoring the importance of weights in complex networks. In this paper, an algorithm for link prediction with link weight assignments is proposed for unweighted networks. First, weights of links are assigned based on the node degree and clustering coefficient, which describe the closeness of two nodes in complex networks; second, the weighted form of similarity index instead of the unweighted form are applied for link prediction; finally, we compare the performance of link predicitions in networks with or without weight assignments. Experiments on four commonly used complex network data sets have been conducted, and the results show that weights of links play an important role in the complex networks, and by assigning weights to links accordingly the algorithm proposed can improve the link prediction performance.