Temporal Motif Signatures for Temporal Graph Neural Networks

• URL: http://arxiv.org/abs/2606.01176v1
• Date: Sun, 31 May 2026 11:32:32 GMT
• Title: Temporal Motif Signatures for Temporal Graph Neural Networks
• Abstract summary: We show that a small four-feature family of past-window star counts already delivers most of the lift over a strong static GNN.<n>Across a wide set of real and synthetic temporal datasets we find that motif activity organizes consistently along three scale-stable axes.<n>A temporal Weisfeiler-Leman analysis places the augmentation relative to the first level of an anchored temporal-WL hierarchy and exhibits a candidate-anchored pair on which motif features distinguish.
• Score: 10.449640808601199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real temporal interaction streams carry predictive structure in short-horizon motif patterns -- repetition, reciprocity, star diversity, triadic flow -- that vanilla temporal graph neural networks (TGNNs) often fail to expose to their edge scorers. We show this concretely on MOOC interaction prediction, where a small four-feature family of past-window star counts already delivers most of the lift over a strong static GNN. Across a wide set of real and synthetic temporal datasets we find that motif activity organizes consistently along three scale-stable axes (dyadic recency/reciprocity, star diversity, triadic flow), and we use this empirical structure to design a compact 13-coordinate, leakage-safe, candidate-local motif feature map h(u, v, t) that linearly embeds into any static or temporal encoder without architectural changes. A temporal Weisfeiler-Leman (WL) analysis places the augmentation relative to the first level of an anchored temporal-WL hierarchy and exhibits a candidate-anchored pair on which motif features distinguish. We demonstrate empirically that the same augmentation consistently lifts performance across heterogeneous tasks: TGB link-property prediction across all five baselines, edge classification on Bitcoin Alpha/OTC and MOOC, and graph-level classification of synthetic temporal generators.

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