Temporal Graph Embedding via Relational Dynamics

Abstract

This paper introduces a novel approach to graph embedding that leverages the temporal dynamics of graph relationships. Traditional graph embedding techniques often treat graphs as static structures, failing to capture the evolving nature of connections between nodes. Our method, Temporal Graph Embedding via Relational Dynamics (TGERD), employs recurrent neural networks (RNNs) to model the sequence of changes in relationships, effectively learning representations of nodes based on their temporal evolution. The core innovation lies in a weighted temporal graph convolution, where recent relationship changes are given higher priority, allowing the model to adapt to shifts in the graph's structure over time. We demonstrate that this approach significantly improves graph embedding quality compared to static embedding methods, particularly in scenarios where relationships are not static. The algorithm is designed for flexibility and can be applied to various graph datasets with evolving connections.