Temporal Graph Neural Networks with Contextual Drift Modeling

Abstract

Graph Neural Networks (GNNs) have achieved remarkable success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a significant limitation of traditional GNNs is their inability to effectively handle evolving graph structures and temporal dependencies. This paper proposes a novel Temporal Graph Neural Network (TGNN) architecture, incorporating a Contextual Drift Model, designed to address this challenge. The TGNN learns and predicts the rate of change in node and edge attributes over time, explicitly modeling the concept of "contextual drift." This information is then integrated into the GNN's message-passing process, allowing the model to adapt dynamically to changing graph dynamics. The core mechanism utilizes a recurrent neural network (RNN) layer to capture the temporal evolution of attributes. The resulting TGNN demonstrates improved performance in scenarios where graph structures and node/edge attributes change over time, offering a more robust and adaptable solution compared to static GNNs. The primary contribution lies in the integration of temporal dynamics and contextual information within a graph neural network framework, paving the way for more sophisticated and reliable graph representations in dynamic environments.