🕸️ Graph Learning

🧠 NeurIPS2026 · 6 paper notes

📌 Same area in other venues: 🎞️ ECCV2026 (1) · 📷 CVPR2026 (8) · 🔬 ICLR2026 (118) · 💬 ACL2026 (24) · 🧪 ICML2026 (35) · 🤖 AAAI2026 (37)

🔥 Top topics: GNNs ×2

Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linear Propagation

The paper maintains nonlinear propagation fixed points on dynamic undirected graphs using residual certificates, endpoint rescaling, and selective pushes, proves deterministic amortized update bounds under degree-normalized error, and shows that nonlinearity can improve static classification accuracy without substantially increasing dynamic propagation cost.

HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration

HoTS generates positive node-wise temperatures from predictive entropy and local homophily estimated by an auxiliary GCN, using an idealized CSBM inverse-homophily temperature law as a structural prior; it preserves predicted classes and achieves the best mean ECE of 4.79% across 18 datasets, but does not win on every dataset or coverage level.

Neural Structural Reasoner: A Brain-inspired Architecture for Reasoning over Structured Knowledge

NSR binds entities, relations, and relation chains to readable network units and connections, performing link prediction through relation-association retrieval and explicit graph traversal; it achieves an MRR of 0.8142 on Nations, but its advantages are dataset-dependent, and the efficiency of its accelerated static implementation should not be equated with that of its online brain-inspired dynamics.

Relation-Aware Graph Foundation Model

REEF treats transferable relation semantics as the unit of graph foundation modeling and uses semantic hypernetworks to generate aggregators, classifiers, and dataset projectors, reaching 48.66%, 48.65%, and 47.72% accuracy on three target graphs under 1-shot classification, while retaining clear limitations for unseen relations, zero-shot tasks, and computational cost.

Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting

SRP preferentially reads weak directions through singular-value soft masks at each frozen GNN layer, supplements features discarded by the original weights through null-space PCA, and adds low-rank prompts after aggregation; it achieves 68.11% accuracy on 5-shot Cora with GraphCL pretraining, but does not outperform the strongest baseline on every dataset.

Understanding and Mitigating Under-Confidence in GNNs from the Final Layer

SCAR explains GNN under-confidence through the final classifier, reduces only final-layer weight decay during training, and moves representations toward predicted-class prototypes at inference time using training-set adjacency groups, reducing ECE across multiple node classification settings without guaranteeing unchanged labels or accuracy.