arXiv:2609.24609v1 Announce Type: new Abstract: Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) prov...
arXiv:2608.20980v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are routinely employed for spatiotemporal forecasting, yet their performance across widely used benchmark datasets is...
arXiv:2510. 09416v4 Announce Type: replace Abstract: Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models.
The paper introduces a diagnostic protocol to disentangle the contributions of parameter sharing and neighborhood access in graph neural networks (GNNs) for electricity load forecasting. By comparing a GNN to three reference models—per-node, shared-parameter without neighbors, and the same GNN without edges—the study evaluates the impact of graph structure across datasets from France, UK feeders, and client consumption series. Results show that parameter sharing often accounts for most of the performance gain, while edges contribute less and can be diminished by degree-preserving rewiring.
arXiv:2608.20980v1 Announce Type: new Abstract: Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the...
arXiv:2607. 07716v1 Announce Type: cross Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy.
The paper introduces the Finance‑Aware Graph Spatio‑Temporal Network (FA‑GSTN) for forecasting realized volatility by treating the implied volatility surface as a dynamic graph. Nodes represent grid points on the surface, with edges capturing adaptive intra‑day spatial and explicit inter‑day temporal relationships, while finance‑aware node features (e.g., option Greeks) and a multi‑scale temporal smoothing gate address high‑frequency noise. Experiments on a large equity options dataset show FA‑GSTN achieves state‑of‑the‑art predictive accuracy (R² up to 0.473) and outperforms Vision Transformer baselines even with only one year of training data, demonstrating robustness during market stress.
arXiv:2606. 27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure.
arXiv:2609.35917v1 Announce Type: new Abstract: Graph neural networks are increasingly applied to road-level crash prediction, but the stability of their reported gains has received little scrutiny....
arXiv:2607. 29293v1 Announce Type: new Abstract: Accurate fault location is critical for distribution network reliability.
Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction. Recent advances in Graph Neural Networks (GNNs) have demonstrated strong perfor- mance by assuming a static graph topology and aggregating information from neighboring series.
arXiv:2608. 06765v1 Announce Type: new Abstract: Continuous-time dynamic graph models predict future links by compressing past interactions into neural states.