Cover Image

Training-free Graph Neural Networks and the Power of Labels as Features

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Sato, Ryoma
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

Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMs
by: Sato, Ryoma
Published: (2026)

Solving the Cold Start Problem on One's Own as an End User via Preference Transfer
by: Sato, Ryoma
Published: (2025)

Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
by: Sato, Ryoma
Published: (2023)

Influential Bandits: Pulling an Arm May Change the Environment
by: Sato, Ryoma, et al.
Published: (2025)

On the Expressive Power of Graph Neural Networks
by: Nalwade, Ashwin, et al.
Published: (2024)

HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
by: Ma, Yihong, et al.
Published: (2023)

User-Side Realization
by: Sato, Ryoma
Published: (2024)

Unveiling the Power of Sparse Neural Networks for Feature Selection
by: Atashgahi, Zahra, et al.
Published: (2024)

Fast EXP3 Algorithms
by: Sato, Ryoma, et al.
Published: (2025)

Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks
by: Dai, Jiazhu, et al.
Published: (2025)

On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social Networks
by: Guettala, Walid, et al.
Published: (2024)

TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks
by: Liu, Yezi, et al.
Published: (2024)

Explicit Feature Interaction-aware Graph Neural Networks
by: Kim, Minkyu, et al.
Published: (2022)

SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids
by: Ghamizi, Salah, et al.
Published: (2024)

Provably Powerful Graph Neural Networks for Directed Multigraphs
by: Egressy, Béni, et al.
Published: (2023)

Graph Neural Networks with Feature and Structure Aware Random Walk
by: Zhuo, Wei, et al.
Published: (2021)

A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks
by: Mandal, Saptarshi, et al.
Published: (2024)

Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks
by: Zhang, Yuxiang, et al.
Published: (2026)

Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation
by: Choi, Yoonhyuk, et al.
Published: (2022)

Towards Generalization of Graph Neural Networks for AC Optimal Power Flow
by: Arowolo, Olayiwola, et al.
Published: (2025)

Accelerating Storage-Based Training for Graph Neural Networks
by: Jang, Myung-Hwan, et al.
Published: (2026)

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
by: Ding, Mucong, et al.
Published: (2024)

Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
by: Wang, Ting, et al.
Published: (2025)

Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
by: Downer, Jane, et al.
Published: (2024)

Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
by: Beddar-Wiesing, Silvia, et al.
Published: (2022)

Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
by: Sommer, Johanna, et al.
Published: (2025)

Oldie but Goodie: Re-illuminating Label Propagation on Graphs with Partially Observed Features
by: Yun, Sukwon, et al.
Published: (2025)

Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
by: Wang, Fali, et al.
Published: (2024)

Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
by: Li, Quan, et al.
Published: (2024)

LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
by: Qiao, Yiran, et al.
Published: (2024)

GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
by: Qiao, Tong, et al.
Published: (2024)

Scalable Neural Network Training over Distributed Graphs
by: Kolluri, Aashish, et al.
Published: (2023)

Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One
by: Zhang, Hongyuan, et al.
Published: (2023)

The Power of Training: How Different Neural Network Setups Influence the Energy Demand
by: Geißler, Daniel, et al.
Published: (2024)

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting
by: Song, Hongjin, et al.
Published: (2025)

Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs
by: Zhang, Taiyan, et al.
Published: (2024)

Reinforcement Learning for Graph Coloring: Understanding the Power and Limits of Non-Label Invariant Representations
by: Cummins, Chase, et al.
Published: (2024)

Graphs Unveiled: Graph Neural Networks and Graph Generation
by: Kovács, László, et al.
Published: (2024)

SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training
by: Gu, Xizhi, et al.
Published: (2024)

Separation Power of Equivariant Neural Networks
by: Pacini, Marco, et al.
Published: (2024)