Relational Graph Embeddings with Temporal Graph Neural Networks
Abstract
Existing graph embedding techniques primarily focus on static graph representations, often neglecting the crucial aspect of temporal dynamics inherent in many real-world graphs. This paper introduces a novel approach combining relational graph embeddings with Temporal Graph Neural Networks (TGNNs) to address this limitation. The core idea is to learn embeddings that adaptively reflect changes in the graph's relational structure over time. We propose a framework where a TGNN is trained to process graph data streams, capturing evolving relationships and generating dynamic embeddings. These embeddings are then utilized for downstream tasks requiring understanding of temporal graph patterns. The proposed method offers a significant advancement over static graph embeddings by incorporating temporal information, leading to improved performance in tasks such as node classification, link prediction, and graph representation learning within dynamic environments. The key contributions lie in the integration of TGNNs with relational graph embeddings and the development of a learning strategy that allows for continuous adaptation to temporal changes.