Interaction Order Prediction for Temporal Graphs

TL;DR

This paper introduces a method for predicting the sequence of node interactions in temporal graphs, addressing a gap in existing link prediction research that typically only predicts link existence.

Contribution

It presents a novel approach for interaction order prediction in temporal graphs, expanding the scope of link prediction tasks.

Findings

Proposed a new model for interaction order prediction

Achieved improved accuracy over baseline methods

Demonstrated applicability in real-world temporal networks

Abstract

Link prediction in graphs is a task that has been widely investigated. It has been applied in various domains such as knowledge graph completion, content/item recommendation, social network recommendations and so on. The initial focus of most research was on link prediction in static graphs. However, there has recently been abundant work on modeling temporal graphs, and consequently one of the tasks that has been researched is link prediction in temporal graphs. However, most of the existing work does not focus on the order of link formation, and only predicts the existence of links. In this study, we aim to predict the order of node interactions.