:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Qiu, Yang, Zou, Yixiong, Wang, Jun, Liu, Wei, Fu, Xiangyu, Li, Ruixuan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.20295
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rethinking Graph Generalization through the Lens of Sharpness-Aware Minimization
by: Qiu, Yang, et al.
Published: (2026)

PAGE: Parametric Generative Explainer for Graph Neural Network
by: Qiu, Yang, et al.
Published: (2024)

Masked Graph Autoencoder with Non-discrete Bandwidths
by: Zhao, Ziwen, et al.
Published: (2024)

Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
by: Liu, Bowen, et al.
Published: (2024)

A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective
by: Zhao, Ziwen, et al.
Published: (2024)

DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
by: Sun, Xin, et al.
Published: (2024)

Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation
by: Tong, Jintao, et al.
Published: (2025)

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
by: Yao, Tianjun, et al.
Published: (2025)

On the Identifiability of Causal Graphs with the Invariance Principle
by: Montagna, Francesco
Published: (2025)

Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
by: Wang, Xiangmeng, et al.
Published: (2026)

Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship
by: Nguyen, Nang Hung, et al.
Published: (2026)

Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
by: Tong, Jintao, et al.
Published: (2025)

GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs
by: Jin, Yue, et al.
Published: (2025)

Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
by: Zhang, Zeyang, et al.
Published: (2023)

CGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization
by: Lu, Bowen, et al.
Published: (2026)

Causal Learning with the Invariance Principle
by: Montagna, Francesco, et al.
Published: (2026)

Towards Subgraph Isomorphism Counting with Graph Kernels
by: Liu, Xin, et al.
Published: (2024)

Subgraph Generation for Generalizing on Out-of-Distribution Links
by: Revolinsky, Jay, et al.
Published: (2025)

Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization
by: Liu, Wei, et al.
Published: (2025)

Methodology and Real-World Applications of Dynamic Uncertain Causality Graph for Clinical Diagnosis with Explainability and Invariance
by: Zhang, Zhan, et al.
Published: (2024)

Handling Distribution Shifts on Graphs: An Invariance Perspective
by: Wu, Qitian, et al.
Published: (2022)

Efficient Causal Structure Learning via Modular Subgraph Integration
by: Sun, Haixiang, et al.
Published: (2026)

Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching
by: Huang, Zibin, et al.
Published: (2024)

Approximate Subgraph Matching with Neural Graph Representations and Reinforcement Learning
by: Li, Kaiyang, et al.
Published: (2026)

ScaleNet: Scale Invariance Learning in Directed Graphs
by: Jiang, Qin, et al.
Published: (2024)

SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
by: Yan, Qi, et al.
Published: (2023)

Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
by: Chen, Xuexin, et al.
Published: (2024)

Graph Out-of-Distribution Generalization via Causal Intervention
by: Wu, Qitian, et al.
Published: (2024)

Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
by: Liu, Dongyi, et al.
Published: (2025)

Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation
by: Liu, Haochen, et al.
Published: (2024)

Neural Graph Navigation for Intelligent Subgraph Matching
by: Ying, Yuchen, et al.
Published: (2025)

Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
by: Ding, Pengfei, et al.
Published: (2024)

Unifying Causal Representation Learning with the Invariance Principle
by: Yao, Dingling, et al.
Published: (2024)

DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization
by: Wang, Jiaqi, et al.
Published: (2025)

Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
by: Susanti, Yuni, et al.
Published: (2025)

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles
by: Chen, Ziang, et al.
Published: (2025)

Model Metamers Reveal Invariances in Graph Neural Networks
by: Xu, Wei, et al.
Published: (2025)

Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
by: He, Yinhan, et al.
Published: (2024)

TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection
by: Chen, Jiankang, et al.
Published: (2024)

Bias as a Virtue: Rethinking Generalization under Distribution Shifts
by: Chen, Ruixuan, et al.
Published: (2025)