GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
Fuente:
arXiv
Guardado en:
| Autores principales: | Park, Namyong, Rossi, Ryan, Wang, Xing, Simoulin, Antoine, Ahmed, Nesreen, Faloutsos, Christos |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Forward Learning of Graph Neural Networks
por: Park, Namyong, et al.
Publicado: (2024)
por: Park, Namyong, et al.
Publicado: (2024)
NetEffect: Discovery and Exploitation of Generalized Network Effects
por: Lee, Meng-Chieh, et al.
Publicado: (2022)
por: Lee, Meng-Chieh, et al.
Publicado: (2022)
Memory-Efficient Fine-Tuning of Transformers via Token Selection
por: Simoulin, Antoine, et al.
Publicado: (2025)
por: Simoulin, Antoine, et al.
Publicado: (2025)
A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models
por: Sahu, Utkarsh, et al.
Publicado: (2025)
por: Sahu, Utkarsh, et al.
Publicado: (2025)
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
por: Evdaimon, Iakovos, et al.
Publicado: (2024)
por: Evdaimon, Iakovos, et al.
Publicado: (2024)
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
por: Gastinger, Julia, et al.
Publicado: (2024)
por: Gastinger, Julia, et al.
Publicado: (2024)
Model Selection with Model Zoo via Graph Learning
por: Li, Ziyu, et al.
Publicado: (2024)
por: Li, Ziyu, et al.
Publicado: (2024)
NetInfoF Framework: Measuring and Exploiting Network Usable Information
por: Lee, Meng-Chieh, et al.
Publicado: (2024)
por: Lee, Meng-Chieh, et al.
Publicado: (2024)
A Unified Graph Selective Prompt Learning for Graph Neural Networks
por: Jiang, Bo, et al.
Publicado: (2024)
por: Jiang, Bo, et al.
Publicado: (2024)
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model
por: Pan, Zhenyu, et al.
Publicado: (2024)
por: Pan, Zhenyu, et al.
Publicado: (2024)
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
por: Luan, Sitao, et al.
Publicado: (2024)
por: Luan, Sitao, et al.
Publicado: (2024)
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
por: Wang, Yuxiang, et al.
Publicado: (2025)
por: Wang, Yuxiang, et al.
Publicado: (2025)
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
por: Zhu, Jing, et al.
Publicado: (2023)
por: Zhu, Jing, et al.
Publicado: (2023)
Robust Graph Contrastive Learning with Information Restoration
por: Zhu, Yulin, et al.
Publicado: (2023)
por: Zhu, Yulin, et al.
Publicado: (2023)
GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets
por: Wang, Junxiang, et al.
Publicado: (2024)
por: Wang, Junxiang, et al.
Publicado: (2024)
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
por: Huang, Shenyang, et al.
Publicado: (2024)
por: Huang, Shenyang, et al.
Publicado: (2024)
HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection
por: Fu, Yali, et al.
Publicado: (2024)
por: Fu, Yali, et al.
Publicado: (2024)
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
por: Li, Zehui, et al.
Publicado: (2023)
por: Li, Zehui, et al.
Publicado: (2023)
A Benchmark for Fairness-Aware Graph Learning
por: Dong, Yushun, et al.
Publicado: (2024)
por: Dong, Yushun, et al.
Publicado: (2024)
LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training
por: Liu, Chenyu, et al.
Publicado: (2026)
por: Liu, Chenyu, et al.
Publicado: (2026)
BANGS: Game-Theoretic Node Selection for Graph Self-Training
por: Wang, Fangxin, et al.
Publicado: (2024)
por: Wang, Fangxin, et al.
Publicado: (2024)
What Do Temporal Graph Learning Models Learn?
por: Hayes, Abigail J., et al.
Publicado: (2025)
por: Hayes, Abigail J., et al.
Publicado: (2025)
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
por: Chung, Hsing-Huan, et al.
Publicado: (2025)
por: Chung, Hsing-Huan, et al.
Publicado: (2025)
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
por: Wang, Zhonghao, et al.
Publicado: (2024)
por: Wang, Zhonghao, et al.
Publicado: (2024)
Feature Selection and Extraction for Graph Neural Networks
por: Acharya, Deepak Bhaskar, et al.
Publicado: (2019)
por: Acharya, Deepak Bhaskar, et al.
Publicado: (2019)
Large Generative Graph Models
por: Wang, Yu, et al.
Publicado: (2024)
por: Wang, Yu, et al.
Publicado: (2024)
Graph Fairness Learning under Distribution Shifts
por: Li, Yibo, et al.
Publicado: (2024)
por: Li, Yibo, et al.
Publicado: (2024)
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation
por: Neo, Neng Kai Nigel, et al.
Publicado: (2024)
por: Neo, Neng Kai Nigel, et al.
Publicado: (2024)
Graph Neural Networks for Source Detection: A Review and Benchmark Study
por: Sterchi, Martin, et al.
Publicado: (2025)
por: Sterchi, Martin, et al.
Publicado: (2025)
Homophily-aware Heterogeneous Graph Contrastive Learning
por: Wang, Haosen, et al.
Publicado: (2025)
por: Wang, Haosen, et al.
Publicado: (2025)
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
por: Park, MoonJeong, et al.
Publicado: (2024)
por: Park, MoonJeong, et al.
Publicado: (2024)
Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
por: Shen, Cencheng, et al.
Publicado: (2023)
por: Shen, Cencheng, et al.
Publicado: (2023)
Graph Contrastive Invariant Learning from the Causal Perspective
por: Mo, Yanhu, et al.
Publicado: (2024)
por: Mo, Yanhu, et al.
Publicado: (2024)
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
por: Huang, Zhenhua, et al.
Publicado: (2024)
por: Huang, Zhenhua, et al.
Publicado: (2024)
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
por: Lin, Junhong, et al.
Publicado: (2024)
por: Lin, Junhong, et al.
Publicado: (2024)
Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
por: Yu, Xincan, et al.
Publicado: (2024)
por: Yu, Xincan, et al.
Publicado: (2024)
A Community-Enhanced Graph Representation Model for Link Prediction
por: Wang, Lei, et al.
Publicado: (2025)
por: Wang, Lei, et al.
Publicado: (2025)
Learning Social Graph for Inactive User Recommendation
por: Liu, Nian, et al.
Publicado: (2024)
por: Liu, Nian, et al.
Publicado: (2024)
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs
por: Wang, Hewen, et al.
Publicado: (2024)
por: Wang, Hewen, et al.
Publicado: (2024)
Enhancing Graph Representation Learning with Localized Topological Features
por: Yan, Zuoyu, et al.
Publicado: (2025)
por: Yan, Zuoyu, et al.
Publicado: (2025)
Ejemplares similares
-
Forward Learning of Graph Neural Networks
por: Park, Namyong, et al.
Publicado: (2024) -
NetEffect: Discovery and Exploitation of Generalized Network Effects
por: Lee, Meng-Chieh, et al.
Publicado: (2022) -
Memory-Efficient Fine-Tuning of Transformers via Token Selection
por: Simoulin, Antoine, et al.
Publicado: (2025) -
A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models
por: Sahu, Utkarsh, et al.
Publicado: (2025) -
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
por: Evdaimon, Iakovos, et al.
Publicado: (2024)