TempHypE-GNN: Hyperbolic Graph Neural ODEs for Hierarchical Temporal Knowledge Graphs
Bibliographic record
Abstract
Pervasive systems such as IoT networks and dynamic knowledge-driven platforms demand models that can capture both temporal evolution and hierarchical structure with high fidelity. While TempHypE introduced a powerful combination of hyperbolic geometry and Neural Ordinary Differential Equations (ODEs) for continuous-time temporal reasoning, it lacks explicit mechanisms to model local graph connectivity and relational propagation. We address this limitation with TempHypE-GNN, a novel extension that integrates hyperbolic graph neural networks (GNNs) into the TempHypE framework. The GNN component enables message passing in hyperbolic space, allowing entities to aggregate neighborhood information across multi-hop relations and improving local relational reasoning under hierarchical constraints. This enhancement complements the global temporal modeling of Neural ODEs, leading to richer representations. TempHypE-GNN jointly embeds entities in the Poincaré ball to capture hierarchical dependencies, applies Neural ODEs to model continuous-time dynamics, and incorporates hyperbolic GNN layers for relational feature propagation. Experimental results on ICEWS14, ICEWS18, and GDELT demonstrate that TempHypE-GNN consistently outperforms both Euclidean models and static hyperbolic baselines. Specifically, it achieves a 17.6% relative improvement in MRR over DySAT on the GDELT dataset and an 8.5% relative increase in Hits@10 over HyperKG on ICEWS18. Furthermore, ablation studies averaged across all benchmarks show that incorporating hyperbolic GNN layers into the TempHypE architecture leads to performance gains of up to 4.3% in MRR and 5.9% in Hits@10, highlighting the added value of relational message passing in hyperbolic space. This demonstrates not only theoretical advances but also the potential for deployment in real-world domains such as IoT, smart-city analytics, and large-scale temporal knowledge management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".