Causal-aware Graph Neural Architecture Search under Distribution Shifts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Peiwen, Wang, Xin, Zhang, Zeyang, Qin, Yijian, Zhang, Ziwei, Wang, Jialong, Li, Yang, Zhu, Wenwu
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
by: Zhang, Zeyang, et al.
Published: (2024)

NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
by: Qin, Yijian, et al.
Published: (2022)

Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision
by: Zhang, Zeyang, et al.
Published: (2024)

Exploring the Potential of Large Language Models in Graph Generation
by: Yao, Yang, et al.
Published: (2024)

Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
by: Xie, Beini, et al.
Published: (2024)

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
by: Li, Peiwen, et al.
Published: (2024)

Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
by: Wang, Xin, et al.
Published: (2022)

Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
by: Zhang, Zeyang, et al.
Published: (2023)

AutoGL: A Library for Automated Graph Learning
by: Zhang, Ziwei, et al.
Published: (2021)

Graphs Generalization under Distribution Shifts
by: Tian, Qin, et al.
Published: (2024)

LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
by: Zhang, Zeyang, et al.
Published: (2023)

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

Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models
by: Wang, Xin, et al.
Published: (2025)

Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
by: Ge, Chendi, et al.
Published: (2025)

Knowledge-aware Evolutionary Graph Neural Architecture Search
by: Wang, Chao, et al.
Published: (2024)

Graph Neural Architecture Search with GPT-4
by: Wang, Haishuai, et al.
Published: (2023)

Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs
by: Qin, Yijian, et al.
Published: (2023)

Graph Fairness Learning under Distribution Shifts
by: Li, Yibo, et al.
Published: (2024)

LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search
by: Gao, Yang, et al.
Published: (2025)

Heterophilic Graph Neural Networks Optimization with Causal Message-passing
by: Wang, Botao, et al.
Published: (2024)

DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach
by: Chen, Qin, et al.
Published: (2025)

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

Alleviating Structural Distribution Shift in Graph Anomaly Detection
by: Gao, Yuan, et al.
Published: (2024)

Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
by: Xu, Xinran, et al.
Published: (2026)

Graph Neural Network Causal Explanation via Neural Causal Models
by: Behnam, Arman, et al.
Published: (2024)

NN-Former: Rethinking Graph Structure in Neural Architecture Representation
by: Xu, Ruihan, et al.
Published: (2025)

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

DPFNAS: Differential Privacy-Enhanced Federated Neural Architecture Search for 6G Edge Intelligence
by: Lv, Yang, et al.
Published: (2025)

NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid Models
by: Xu, Yang, et al.
Published: (2024)

Causal Graph Neural Networks for Healthcare
by: Mesinovic, Munib, et al.
Published: (2025)

Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts
by: Yang, Jinluan, et al.
Published: (2024)

CausalGuard: Conformal Inference under Graph Uncertainty
by: Singh, Vikash, et al.
Published: (2026)

FnRGNN: Distribution-aware Fairness in Graph Neural Network
by: Park, Soyoung, et al.
Published: (2025)

Multi-Objective Neural Architecture Search by Learning Search Space Partitions
by: Zhao, Yiyang, et al.
Published: (2024)

Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
by: Zhang, Sirui, et al.
Published: (2026)

Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
by: Wang, Song, et al.
Published: (2025)

Heterogeneous Graph Neural Networks with Loss-decrease-aware Curriculum Learning
by: Wang, Yili
Published: (2024)

Risk-aware Direct Preference Optimization under Nested Risk Measure
by: Zhang, Lijun, et al.
Published: (2025)

Transferrable Surrogates in Expressive Neural Architecture Search Spaces
by: Qin, Shiwen, et al.
Published: (2025)

GraDE: A Graph Diffusion Estimator for Frequent Subgraph Discovery in Neural Architectures
by: Yang, Yikang, et al.
Published: (2026)