Temporal Sheaf Neural Networks Advance Link Prediction in Dynamic Graphs
Researchers have introduced Temporal Sheaf Neural Networks (TSNN), a framework for predicting links in dynamic graphs by equipping each node with a time-varying local coordinate system and comparing node states only after explicit geometric transport between those systems. Unlike existing continuous-time graph models that rely on a shared global embedding space, TSNN captures node-specific and evolving interaction semantics through dynamic local frames parameterized via low-rank Householder products. The approach achieves competitive or superior performance on multiple standard benchmarks, with the largest gains on graphs where nodes play heterogeneous roles.
TSNN, proposed by Tanzila Khan and collaborators in a preprint submitted to arXiv on June 8, 2026, addresses a core limitation of current temporal graph learning models: the assumption that all nodes share a common embedding geometry. The framework assigns each node a time-varying orthogonal frame and transports node representations into a common local coordinate system before computing interactions, ensuring that comparisons are geometrically meaningful. Frames are updated efficiently using low-rank Householder products, and hidden states are preserved exactly under frame changes, avoiding information loss. A geometric-residual decoder anchors predictions on transported distances while learning residual corrections, and all computations are strictly causal, using only pre-event history. The authors provide theoretical guarantees showing that the full-active diffusion used by TSNN corresponds to a metric-gradient descent step on the combinatorial sheaf Dirichlet energy, with monotone-descent and non-expansiveness properties. Evaluations on the TGB v2, temporal-heterogeneous, and DGB benchmark suites show TSNN matches or outperforms the strongest prior methods on most tasks, with ablations confirming the individual contributions of dynamic frames, orthogonal transport, and geometric-residual decoding.
What's missing
As a preprint, this work has not yet undergone peer review, so the theoretical claims and empirical results have not been independently validated. The paper does not report computational cost or wall-clock training time comparisons against baselines, leaving scalability to very large graphs an open question. It is also unclear how sensitive performance is to the choice of Householder rank hyperparameter across different graph types.
What different sources said
- arXiv cs.LGCenter
Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport
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