Copertina

Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Xiyuan, Li, Pan, Zhang, Muhan
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally
di: Wang, Xiyuan, et al.
Pubblicazione: (2025)

Graph as Point Set
di: Wang, Xiyuan, et al.
Pubblicazione: (2024)

Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
di: Zhou, Cai, et al.
Pubblicazione: (2024)

Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
di: Zhou, Junru, et al.
Pubblicazione: (2024)

Efficient Neural Common Neighbor for Temporal Graph Link Prediction
di: Zhang, Xiaohui, et al.
Pubblicazione: (2024)

Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
di: Liu, Yewei, et al.
Pubblicazione: (2025)

Neural Common Neighbor with Completion for Link Prediction
di: Wang, Xiyuan, et al.
Pubblicazione: (2023)

GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model
di: Yang, Haotong, et al.
Pubblicazione: (2024)

GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
di: Wang, Juntong, et al.
Pubblicazione: (2026)

Geometric Representation Condition Improves Equivariant Molecule Generation
di: Li, Zian, et al.
Pubblicazione: (2024)

OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction
di: Wang, Juntong, et al.
Pubblicazione: (2025)

Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?
di: Wang, Xiyuan, et al.
Pubblicazione: (2025)

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
di: Ma, Weishuo, et al.
Pubblicazione: (2025)

Is Distance Matrix Enough for Geometric Deep Learning?
di: Li, Zian, et al.
Pubblicazione: (2023)

On the Completeness of Invariant Geometric Deep Learning Models
di: Li, Zian, et al.
Pubblicazione: (2024)

Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
di: Zhou, Junru, et al.
Pubblicazione: (2023)

Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
di: Kong, Lecheng, et al.
Pubblicazione: (2025)

Reconsidering the Performance of GAE in Link Prediction
di: Ma, Weishuo, et al.
Pubblicazione: (2024)

On Lexical Invariance on Multisets and Graphs
di: Zhang, Muhan
Pubblicazione: (2024)

CktGNN: Circuit Graph Neural Network for Electronic Design Automation
di: Dong, Zehao, et al.
Pubblicazione: (2023)

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
di: Feng, Jiarui, et al.
Pubblicazione: (2023)

Griffin: Towards a Graph-Centric Relational Database Foundation Model
di: Wang, Yanbo, et al.
Pubblicazione: (2025)

SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels
di: Dong, Xiangyu, et al.
Pubblicazione: (2025)

Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
di: Li, Shuangjie, et al.
Pubblicazione: (2024)

CanvasMAR: Improving Masked Autoregressive Video Prediction With Canvas
di: Li, Zian, et al.
Pubblicazione: (2025)

Graph Neural Networks for Graphs with Heterophily: A Survey
di: Zheng, Xin, et al.
Pubblicazione: (2022)

Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
di: Zhu, Dingyuan, et al.
Pubblicazione: (2024)

GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
di: Yan, Shaoheng, et al.
Pubblicazione: (2025)

One for All: Towards Training One Graph Model for All Classification Tasks
di: Liu, Hao, et al.
Pubblicazione: (2023)

All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
di: Sun, Xiangguo, et al.
Pubblicazione: (2024)

Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
di: Chen, Qingfeng, et al.
Pubblicazione: (2025)

When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections
di: Chelouche, Keryan, et al.
Pubblicazione: (2024)

SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification
di: Varghese, Alan John, et al.
Pubblicazione: (2024)

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
di: He, Jiajun, et al.
Pubblicazione: (2025)

Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
di: Wo, Zengyi, et al.
Pubblicazione: (2025)

OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks
di: Li, Haoyang, et al.
Pubblicazione: (2025)

Fed-Listing: Federated Label Distribution Inference in Graph Neural Networks
di: Nakarmi, Suprim, et al.
Pubblicazione: (2026)

Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
di: Gao, Wanfu, et al.
Pubblicazione: (2025)

Leveraging Task Structures for Improved Identifiability in Neural Network Representations
di: Chen, Wenlin, et al.
Pubblicazione: (2023)

On the Stability of Expressive Positional Encodings for Graphs
di: Huang, Yinan, et al.
Pubblicazione: (2023)