TempoKGAT: A Novel Graph Attention Network Approach for Temporal Graph Analysis
This incremental work addresses the problem of analyzing temporal graphs for researchers and practitioners in domains like traffic, energy, and health.
The paper tackled the limited application of graph neural networks to dynamic, temporal data by introducing TempoKGAT, a graph attention network that combines time-decaying weights and selective neighbor aggregation, achieving superior accuracy on traffic, energy, and health datasets compared to state-of-the-art methods.
Graph neural networks (GNN) have shown significant capabilities in handling structured data, yet their application to dynamic, temporal data remains limited. This paper presents a new type of graph attention network, called TempoKGAT, which combines time-decaying weight and a selective neighbor aggregation mechanism on the spatial domain, which helps uncover latent patterns in the graph data. In this approach, a top-k neighbor selection based on the edge weights is introduced to represent the evolving features of the graph data. We evaluated the performance of our TempoKGAT on multiple datasets from the traffic, energy, and health sectors involving spatio-temporal data. We compared the performance of our approach to several state-of-the-art methods found in the literature on several open-source datasets. Our method shows superior accuracy on all datasets. These results indicate that TempoKGAT builds on existing methodologies to optimize prediction accuracy and provide new insights into model interpretation in temporal contexts.