Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Yuxiang, Dai, Xinnan, Fan, Wenqi, Ma, Yao |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
by: Wang, Zhonghao, et al.
Published: (2024)
by: Wang, Zhonghao, et al.
Published: (2024)
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
A Benchmark for Fairness-Aware Graph Learning
by: Dong, Yushun, et al.
Published: (2024)
by: Dong, Yushun, et al.
Published: (2024)
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
by: Park, Namyong, et al.
Published: (2024)
by: Park, Namyong, et al.
Published: (2024)
Graph Machine Learning in the Era of Large Language Models (LLMs)
by: Wang, Shijie, et al.
Published: (2024)
by: Wang, Shijie, et al.
Published: (2024)
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
by: Luan, Sitao, et al.
Published: (2024)
by: Luan, Sitao, et al.
Published: (2024)
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
by: Gastinger, Julia, et al.
Published: (2024)
by: Gastinger, Julia, et al.
Published: (2024)
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
by: Hashemi, Mohammad, et al.
Published: (2024)
by: Hashemi, Mohammad, et al.
Published: (2024)
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
by: Wang, Zehong, et al.
Published: (2024)
by: Wang, Zehong, et al.
Published: (2024)
Lying Graph Convolution: Learning to Lie for Node Classification Tasks
by: Castellana, Daniele
Published: (2024)
by: Castellana, Daniele
Published: (2024)
A Comprehensive Review of Community Detection in Graphs
by: Li, Jiakang, et al.
Published: (2023)
by: Li, Jiakang, et al.
Published: (2023)
Graph Contrastive Invariant Learning from the Causal Perspective
by: Mo, Yanhu, et al.
Published: (2024)
by: Mo, Yanhu, et al.
Published: (2024)
A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
by: Gao, Zhangyang, et al.
Published: (2024)
by: Gao, Zhangyang, et al.
Published: (2024)
Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
by: Liu, Yonghao, et al.
Published: (2025)
by: Liu, Yonghao, et al.
Published: (2025)
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
by: Li, Dongyue, et al.
Published: (2023)
by: Li, Dongyue, et al.
Published: (2023)
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
by: Wu, Man, et al.
Published: (2024)
by: Wu, Man, et al.
Published: (2024)
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
by: Li, Zehui, et al.
Published: (2023)
by: Li, Zehui, et al.
Published: (2023)
GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets
by: Wang, Junxiang, et al.
Published: (2024)
by: Wang, Junxiang, et al.
Published: (2024)
Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias
by: He, Xin, et al.
Published: (2024)
by: He, Xin, et al.
Published: (2024)
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
by: Wu, Yuhao, et al.
Published: (2024)
by: Wu, Yuhao, et al.
Published: (2024)
When Do LLMs Help With Node Classification? A Comprehensive Analysis
by: Wu, Xixi, et al.
Published: (2025)
by: Wu, Xixi, et al.
Published: (2025)
Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data
by: Yuan, Hanyang, et al.
Published: (2024)
by: Yuan, Hanyang, et al.
Published: (2024)
Graph Foundation Models: A Comprehensive Survey
by: Wang, Zehong, et al.
Published: (2025)
by: Wang, Zehong, et al.
Published: (2025)
A Comprehensive Data-centric Overview of Federated Graph Learning
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks
by: Dong, Yuxin, et al.
Published: (2024)
by: Dong, Yuxin, et al.
Published: (2024)
Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training
by: He, Yufei, et al.
Published: (2024)
by: He, Yufei, et al.
Published: (2024)
Learning Social Graph for Inactive User Recommendation
by: Liu, Nian, et al.
Published: (2024)
by: Liu, Nian, et al.
Published: (2024)
LEGO-Learn: Label-Efficient Graph Open-Set Learning
by: Xu, Haoyan, et al.
Published: (2024)
by: Xu, Haoyan, et al.
Published: (2024)
A Unified Graph Selective Prompt Learning for Graph Neural Networks
by: Jiang, Bo, et al.
Published: (2024)
by: Jiang, Bo, et al.
Published: (2024)
Enhancing Graph Representation Learning with Localized Topological Features
by: Yan, Zuoyu, et al.
Published: (2025)
by: Yan, Zuoyu, et al.
Published: (2025)
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
by: Wang, Haohui, et al.
Published: (2023)
by: Wang, Haohui, et al.
Published: (2023)
Graph Neural Networks for Source Detection: A Review and Benchmark Study
by: Sterchi, Martin, et al.
Published: (2025)
by: Sterchi, Martin, et al.
Published: (2025)
To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders
by: Salha-Galvan, Guillaume, et al.
Published: (2025)
by: Salha-Galvan, Guillaume, et al.
Published: (2025)
Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach
by: Li, Shujing, et al.
Published: (2024)
by: Li, Shujing, et al.
Published: (2024)
The Role of Community Detection Methods in Performance Variations of Graph Mining Tasks
by: Ghosh, Shrabani, et al.
Published: (2025)
by: Ghosh, Shrabani, et al.
Published: (2025)
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024)
by: Huang, Zhenhua, et al.
Published: (2024)
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation
by: Neo, Neng Kai Nigel, et al.
Published: (2024)
by: Neo, Neng Kai Nigel, et al.
Published: (2024)
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
by: Dong, Kaiwen, et al.
Published: (2023)
by: Dong, Kaiwen, et al.
Published: (2023)
Forward Learning of Graph Neural Networks
by: Park, Namyong, et al.
Published: (2024)
by: Park, Namyong, et al.
Published: (2024)
Homophily-aware Heterogeneous Graph Contrastive Learning
by: Wang, Haosen, et al.
Published: (2025)
by: Wang, Haosen, et al.
Published: (2025)
Similar Items
-
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
by: Wang, Zhonghao, et al.
Published: (2024) -
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024) -
A Benchmark for Fairness-Aware Graph Learning
by: Dong, Yushun, et al.
Published: (2024) -
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
by: Park, Namyong, et al.
Published: (2024) -
Graph Machine Learning in the Era of Large Language Models (LLMs)
by: Wang, Shijie, et al.
Published: (2024)