Link Prediction Evaluation Using Palette Weisfeiler-Lehman Graph Labelling Algorithm

Abstract

Link prediction is gaining interest in the community of machine learning due to its popularity in the applications such as in social networking and e-commerce. This paper aims to present the performance of link prediction using a set of predictive models. In link prediction modelling, feature extraction is a challenging issue and some simple heuristics such as common-neighbors and Katz index were commonly used. Here, palette weisfeiler-lehman graph labelling algorithms have been used, which has a few advantages such as it has order-preserving properties and provides better computational efficiency. Whereas, other feature extraction algorithms cannot preserve the order of the vertices in the subgraph, and also take more computational time. The features were extracted in two ways with the number of vertices in each subgraph, say K = 10 and K = 15. The extracted features were fitted to a range of classifiers. Further, the performance has been obtained on the basis of the area under the curve (AUC) measure. Comparative analysis of all the classifiers based on the AUC results has been presented to determine which predictive model provides better performance across all the networks. This leads to the conclusion that ADABoost, Bagging and Adaptive Logistic Regression performed well almost on all the network. Lastly, comparative analysis of 12 existing methods with three best predictive models has been done to show that link prediction with predictive models performs well across different kinds of networks.