StreamTGN: A GPU-Efficient Serving System for Streaming Temporal Graph Neural Networks

Temporal Graph Neural Networks (TGNs) achieve state-of-the-art performance on dynamic graph tasks, yet existing systems focus exclusively on accelerating training – at inference time, every new edge triggers $O(\mathrm{\mid }V\mathrm{\mid })$ embedding updates even though only a small fraction of nodes are affected. We present $\mathbf{\text{StreamTGN}}$, the first streaming TGN inference system exploiting the inherent locality of temporal graph updates: in an $L$-layer TGN, a new edge affects only nodes within $L$ hops of the endpoints, typically less than 0.2% on million-node graphs. StreamTGN maintains persistent GPU-resident node memory and uses dirty-flag propagation to identify the affected set $\mathcal{A}$, reducing per-batch complexity from $O(\mathrm{\mid }V\mathrm{\mid })$ to $O(\mathrm{\mid }\mathcal{A}\mathrm{\mid })$ with zero accuracy loss. Drift-aware adaptive rebuild scheduling and batched streaming with relaxed ordering further maximize throughput. Experiments on eight temporal graphs (2K–2.6M nodes) show 4.5$\times$–739$\times$ speedup for TGN and up to 4,207$\times$ for TGAT, with identical accuracy. StreamTGN is orthogonal to training optimizations: combining SWIFT with StreamTGN yields 24$\times$ end-to-end speedup across three architectures (TGN, TGAT, DySAT).