Image de couverture de livre

Structural Invariance Matters: Rethinking Graph Rewiring through Graph Metrics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Benoit, Alexandre, Aitken, Catherine, He, Yu
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks
par: Attali, Hugo, et autres
Publié: (2025)

SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
par: Yan, Qi, et autres
Publié: (2023)

Temporal Graph Rewiring with Expander Graphs
par: Petrović, Katarina, et autres
Publié: (2024)

Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning
par: Zhang, Yihan, et autres
Publié: (2026)

On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks
par: Chehreghani, Mostafa Haghir
Publié: (2026)

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
par: Attali, Hugo, et autres
Publié: (2024)

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
par: Attali, Hugo, et autres
Publié: (2026)

Handling Distribution Shifts on Graphs: An Invariance Perspective
par: Wu, Qitian, et autres
Publié: (2022)

Rethinking Link Prediction for Directed Graphs
par: He, Mingguo, et autres
Publié: (2025)

Rethinking Graph Contrastive Learning through Relative Similarity Preservation
par: Ning, Zhiyuan, et autres
Publié: (2025)

GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
par: Zeng, Fanlong, et autres
Publié: (2025)

Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulations
par: Seo, Sangwoo, et autres
Publié: (2025)

Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners
par: Herbst, Daniel, et autres
Publié: (2025)

Rethinking Tokenized Graph Transformers for Node Classification
par: Chen, Jinsong, et autres
Publié: (2025)

NN-Former: Rethinking Graph Structure in Neural Architecture Representation
par: Xu, Ruihan, et autres
Publié: (2025)

Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
par: Miao, Rui, et autres
Publié: (2024)

Accurate and Scalable Graph Neural Networks via Message Invariance
par: Shi, Zhihao, et autres
Publié: (2025)

Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
par: Nguyen, Nang Hung, et autres
Publié: (2026)

GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
par: Bresson, Roman, et autres
Publié: (2026)

Rethinking Propagation for Unsupervised Graph Domain Adaptation
par: Liu, Meihan, et autres
Publié: (2024)

GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
par: Duan, Shijin, et autres
Publié: (2024)

Representation Invariance and Allocation: When Subgroup Balance Matters
par: Alloula, Anissa, et autres
Publié: (2025)

Graph-Based Fraud Detection with Dual-Path Graph Filtering
par: He, Wei, et autres
Publié: (2026)

Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning
par: Jian, Xiangru, et autres
Publié: (2024)

A Metric for the Balance of Information in Graph Learning
par: Davies, Alex O., et autres
Publié: (2025)

Metric Based Few-Shot Graph Classification
par: Crisostomi, Donato, et autres
Publié: (2022)

Towards Metric-Faithful Neural Graph Matching
par: Shivottam, Jyotirmaya, et autres
Publié: (2026)

GraphEdit: Large Language Models for Graph Structure Learning
par: Guo, Zirui, et autres
Publié: (2024)

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
par: Gao, Xinyi, et autres
Publié: (2024)

Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
par: Waseda, Futa, et autres
Publié: (2024)

Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
par: Zou, Qi, et autres
Publié: (2024)

Speeding Up MACE: Low-Precision Tricks for Equivarient Force Fields
par: Benoit, Alexandre
Publié: (2025)

Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
par: Downer, Jane, et autres
Publié: (2024)

Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
par: Zhou, Cai, et autres
Publié: (2026)

LoReC: Rethinking Large Language Models for Graph Data Analysis
par: Zhan, Hongyu, et autres
Publié: (2026)

Continuous Invariance Learning
par: Lin, Yong, et autres
Publié: (2023)

Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering
par: Guo, Jingwei, et autres
Publié: (2024)

From Variance to Invariance: Qualitative Content Analysis for Narrative Graph Annotation
par: Huang, Junbo, et autres
Publié: (2026)

Heterogeneous Graph Structure Learning through the Lens of Data-generating Processes
par: Jiang, Keyue, et autres
Publié: (2025)

Rethinking Fair Graph Neural Networks from Re-balancing
par: Li, Zhixun, et autres
Publié: (2024)