arXiv:2607. 10804v1 Announce Type: new Abstract: Graph neural networks (GNNs) are increasingly deployed in real-world applications where distribution shift is un-avoidable.
arXiv:2606. 08467v1 Announce Type: cross Abstract: While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to adversarial structural perturbations remains largely unexplored.
arXiv:2610. 09020v1 Announce Type: new Abstract: Modern neural networks are often miscalibrated, with a tendency to overconfidence.
The paper introduces a post-hoc diagnostic for graph neural networks that freezes a trained model, fits a linear probe to each layer, and tracks probe accuracy with normalized predictive entropy. By examining layer-wise probe entropy, the authors can distinguish beneficial, neutral, and harmful smoothing effects—something existing geometric measures cannot do. The study also shows that entropy degradation patterns separate two main GNN failure modes: structural mismatch under heterophily, which appears early even in shallow models, and excessive-depth oversmoothing that emerges only as propagation depth increases.
The study investigates how different message‑passing (MP) layers affect graph‑level regression performance in Graph Neural Networks (GNNs). By replacing the MP component in four existing GNN regressors with ten varied configurations—including convolutional, isomorphism‑based, and attention‑based designs—the authors evaluate performance across eleven neural‑network graph datasets, measuring metrics such as rank correlation, prediction error, top‑k retrieval, latency, and memory usage. Results show that the choice of MP layer significantly influences outcomes, with the optimal layer depending on graph size, training‑set size, and regression objective; classical layers like GEN, k‑GNN, and PNA outperform attention‑based layers on small graphs, while GATv2 excels on datasets with few large graphs.
arXiv:2607. 05017v1 Announce Type: cross Abstract: The performance of deep learning models crucially depends on the settings of hyperparameters like learning rate, initialization scale, and weight decay.
arXiv:2602. 09258v2 Announce Type: replace Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations.
arXiv:2607. 21885v1 Announce Type: new Abstract: Coarsening-based training for graph neural networks (GNNs), i.
arXiv:2607. 26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints.
arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.
arXiv:2607. 27767v1 Announce Type: new Abstract: Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques for even larger graphs.
arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and c...