Copertina

You do not have to train Graph Neural Networks at all on text-attributed graphs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Dong, Kaiwen, Guo, Zhichun, Chawla, Nitesh V.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Universal Link Predictor By In-Context Learning on Graphs
di: Dong, Kaiwen, et al.
Pubblicazione: (2024)

CORE: Data Augmentation for Link Prediction via Information Bottleneck
di: Dong, Kaiwen, et al.
Pubblicazione: (2024)

Pure Message Passing Can Estimate Common Neighbor for Link Prediction
di: Dong, Kaiwen, et al.
Pubblicazione: (2023)

ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery
di: Huang, Xiaobao, et al.
Pubblicazione: (2025)

Do LLMs have core beliefs?
di: Sokol, Anna, et al.
Pubblicazione: (2026)

YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training
di: Li, Yi, et al.
Pubblicazione: (2024)

Node Duplication Improves Cold-start Link Prediction
di: Guo, Zhichun, et al.
Pubblicazione: (2024)

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

The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data
di: Dablain, Damien A., et al.
Pubblicazione: (2024)

Can we Soft Prompt LLMs for Graph Learning Tasks?
di: Liu, Zheyuan, et al.
Pubblicazione: (2024)

AnyLoss: Transforming Classification Metrics into Loss Functions
di: Han, Doheon, et al.
Pubblicazione: (2024)

Intersectional Divergence: Measuring Fairness in Regression
di: Germino, Joe, et al.
Pubblicazione: (2025)

Beyond Message Passing: Neural Graph Pattern Machine
di: Wang, Zehong, et al.
Pubblicazione: (2025)

HONEM: Learning Embedding for Higher Order Networks
di: Saebi, Mandana, et al.
Pubblicazione: (2019)

TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
di: Feng, Jiarui, et al.
Pubblicazione: (2024)

Spectral Manifold Harmonization for Graph Imbalanced Regression
di: Nogueira, Brenda, et al.
Pubblicazione: (2025)

Conformalized Selective Regression
di: Sokol, Anna, et al.
Pubblicazione: (2024)

Graph Neural Prompting with Large Language Models
di: Tian, Yijun, et al.
Pubblicazione: (2023)

AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
di: Guo, Taicheng, et al.
Pubblicazione: (2026)

A Survey of Large Language Models for Graphs
di: Ren, Xubin, et al.
Pubblicazione: (2024)

Fast Explanations via Policy Gradient-Optimized Explainer
di: Pan, Deng, et al.
Pubblicazione: (2024)

UGMAE: A Unified Framework for Graph Masked Autoencoders
di: Tian, Yijun, et al.
Pubblicazione: (2024)

GFT: Graph Foundation Model with Transferable Tree Vocabulary
di: Wang, Zehong, et al.
Pubblicazione: (2024)

AgentDrug: Utilizing Large Language Models in An Agentic Workflow for Zero-Shot Molecular Editing
di: Le, Khiem, et al.
Pubblicazione: (2024)

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE
di: Le, Khiem, et al.
Pubblicazione: (2025)

Safety in Graph Machine Learning: Threats and Safeguards
di: Wang, Song, et al.
Pubblicazione: (2024)

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
di: Wang, Zehong, et al.
Pubblicazione: (2024)

Mobile Network Configuration Recommendation using Deep Generative Graph Neural Network
di: Piroti, Shirwan, et al.
Pubblicazione: (2024)

Graph Diffusion Transformers are In-Context Molecular Designers
di: Liu, Gang, et al.
Pubblicazione: (2025)

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond
di: Guo, Kehan, et al.
Pubblicazione: (2025)

SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression
di: Nogueira, Brenda, et al.
Pubblicazione: (2025)

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
di: He, Xiaoxin, et al.
Pubblicazione: (2024)

Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
di: Duan, Yuying, et al.
Pubblicazione: (2024)

Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control
di: Carvalho, Tânia, et al.
Pubblicazione: (2022)

Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis
di: Ma, Yijun, et al.
Pubblicazione: (2026)

Enhanced Pre-training of Graph Neural Networks for Million-Scale Heterogeneous Graphs
di: Sun, Shengyin, et al.
Pubblicazione: (2025)

Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models
di: Nogueira, Brenda, et al.
Pubblicazione: (2026)

A Graph Laplacian Eigenvector-based Pre-training Method for Graph Neural Networks
di: Dai, Howard, et al.
Pubblicazione: (2025)

Unifews: You Need Fewer Operations for Efficient Graph Neural Networks
di: Liao, Ningyi, et al.
Pubblicazione: (2024)

Bundle Neural Networks for message diffusion on graphs
di: Bamberger, Jacob, et al.
Pubblicazione: (2024)