Temporal Graph Embeddings via Relational Dynamics

Abstract

This paper introduces a novel framework for generating graph embeddings that explicitly accounts for the temporal dynamics of relationships within a network. Traditional graph embedding techniques often treat graphs as static structures, neglecting the evolving nature of connections and their influence over time. Our approach leverages a recurrent neural network (RNN) coupled with a custom-designed loss function that measures "temporal divergence" – the difference in embeddings of nodes at consecutive time steps – weighted by the strength and type of relationships. This allows the model to learn and represent how relationships change over time, leading to more accurate and robust graph embeddings. We argue that this method represents a significant advancement over existing static embedding techniques and offers the potential to capture emergent network behaviors and predict future network modifications with improved accuracy. The core claim of this work is that incorporating temporal dynamics dramatically enhances graph embedding capabilities. ---