Spectral Gap-Driven Coarsening for Dynamic Graph Neural Networks

Dynamic Graph Neural Networks (DGNNs) suffer from a significant scalability bottleneck due to high computational demands resulting from their innate design to aggregate information both over graph topology and over time. While graph coarsening has successfully mitigated these costs for static graph neural networks, its potential remains largely untapped in the dynamic setting. Bridging this gap is particularly challenging because different DGNN architectures in literature aggregate information across structural topologies and temporal dimensions in different manners. %Hence, we require a coarsening method that can adapt to the complexity of a system evolving over time in different manners.