Repeating Link Prediction over Dynamic Graphs

Abstract

Graphs are a vastly useful and widely used form of modeling and representation of systems, processes, entities, events, objects, components etc., in various domains of discourse, that reflects relations or connections of modeled entities. Graphs are vital to diverse data mining applications, as they capture relationships between data items, such as dependencies or interactions, and graph analysis can reveal valuable insights for many application domains including machine learning, anomaly detection, clustering, recommendations, social influence analysis, bioinformatics, and others. The analysis of the evolutionary behavior of dynamic graphs provides the means to continuously predict the appearance, and also, the disappearance of new graph links, i.e., to perform the Dynamic Link Prediction Task. Dynamic Link Prediction has been explored widely in the past years; however, the majority of these works focus on discovering new edges (by implicitly assuming ever growing dynamic networks). However, very few works focus on the repeating edges, i.e., links that continuously vanish and reappear in the dynamic network, but which size (in terms of number of nodes and edges) does not significantly change over long periods of time. In this work, we first study the literature for link prediction in the static settlement, then, we focus on dynamic link prediction, underlining the strengths and weaknesses of every approach studied. We discover that traditional methods do not work well with repeating links as they are unable to encode temporal patterns associated with the edges while also considering the topological graph features. We propose a novel method, Temporal Edge Embedding Neural Network (TEEN), which is based on a deep learning architecture that jointly optimizes the prediction of the correct edge labels as well as the proximity of two nodes’ pairs in their latent space at every time step. Our solution benefits of node embeddings created with deep encoders from where an edge embedding is created for every time step. Our evaluation experiments on transactional graphs show that TEEN is able to outperform state-of-the-art models by over 8% on AUC and over 7% on F1-Score. We show that our approach brings significant improvements in the scenario of transactional graphs.