Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
Fuente:
arXiv
Saved in:
| Main Authors: | Gupta, Mridul, Jain, Samyak, Ramani, Vansh, Kodamana, Hariprasad, Ranu, Sayan |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bonsai: Gradient-free Graph Condensation for Node Classification
by: Gupta, Mridul, et al.
Published: (2024)
by: Gupta, Mridul, et al.
Published: (2024)
Mirage: Model-Agnostic Graph Distillation for Graph Classification
by: Gupta, Mridul, et al.
Published: (2023)
by: Gupta, Mridul, et al.
Published: (2023)
On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026)
by: Ramani, Vansh, et al.
Published: (2026)
Panorama: Fast-Track Nearest Neighbors
by: Ramani, Vansh, et al.
Published: (2025)
by: Ramani, Vansh, et al.
Published: (2025)
GNNMerge: Merging of GNN Models Without Accessing Training Data
by: Garg, Vipul, et al.
Published: (2025)
by: Garg, Vipul, et al.
Published: (2025)
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
by: Rani, Jyoti, et al.
Published: (2024)
by: Rani, Jyoti, et al.
Published: (2024)
NeuroCUT: A Neural Approach for Robust Graph Partitioning
by: Shah, Rishi, et al.
Published: (2023)
by: Shah, Rishi, et al.
Published: (2023)
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
by: Sirohi, Anuj Kumar, et al.
Published: (2024)
by: Sirohi, Anuj Kumar, et al.
Published: (2024)
EUGENE: Explainable Structure-aware Graph Edit Distance Estimation with Generalized Edit Costs
by: Bommakanti, Aditya, et al.
Published: (2024)
by: Bommakanti, Aditya, et al.
Published: (2024)
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
by: Bishnoi, Suresh, et al.
Published: (2022)
by: Bishnoi, Suresh, et al.
Published: (2022)
GRAIL: Graph Edit Distance and Node Alignment Using LLM-Generated Code
by: Verma, Samidha, et al.
Published: (2025)
by: Verma, Samidha, et al.
Published: (2025)
GnnXemplar: Exemplars to Explanations -- Natural Language Rules for Global GNN Interpretability
by: Armgaan, Burouj, et al.
Published: (2025)
by: Armgaan, Burouj, et al.
Published: (2025)
Revealing Interpretable Failure Modes of VLMs
by: Chaudhary, Isha, et al.
Published: (2026)
by: Chaudhary, Isha, et al.
Published: (2026)
LightTopoGAT: Enhancing Graph Attention Networks with Topological Features for Efficient Graph Classification
by: Sharma, Ankit, et al.
Published: (2025)
by: Sharma, Ankit, et al.
Published: (2025)
Safe Langevin Soft Actor Critic
by: Keswani, Mahesh, et al.
Published: (2026)
by: Keswani, Mahesh, et al.
Published: (2026)
Predicting Soil Macronutrient Levels: A Machine Learning Approach Models Trained on pH, Conductivity, and Average Power of Acid-Base Solutions
by: Kumar, Mridul, et al.
Published: (2025)
by: Kumar, Mridul, et al.
Published: (2025)
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
by: Kosan, Mert, et al.
Published: (2023)
by: Kosan, Mert, et al.
Published: (2023)
Training-free Heterogeneous Graph Condensation via Data Selection
by: Liang, Yuxuan, et al.
Published: (2024)
by: Liang, Yuxuan, et al.
Published: (2024)
A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models
by: Sharma, Vansh, et al.
Published: (2023)
by: Sharma, Vansh, et al.
Published: (2023)
Integrating Domain Knowledge for Financial QA: A Multi-Retriever RAG Approach with LLMs
by: Zhang, Yukun, et al.
Published: (2025)
by: Zhang, Yukun, et al.
Published: (2025)
Development of Pre-Trained Transformer-based Models for the Nepali Language
by: Thapa, Prajwal, et al.
Published: (2024)
by: Thapa, Prajwal, et al.
Published: (2024)
MDPs with a State Sensing Cost
by: Kapoor, Vansh, et al.
Published: (2025)
by: Kapoor, Vansh, et al.
Published: (2025)
Designing an Intelligent Parcel Management System using IoT & Machine Learning
by: Gupta, Mohit, et al.
Published: (2024)
by: Gupta, Mohit, et al.
Published: (2024)
Training Robust Graph Neural Networks by Modeling Noise Dependencies
by: In, Yeonjun, et al.
Published: (2025)
by: In, Yeonjun, et al.
Published: (2025)
CAPA: Contribution-Aware Pruning and FFN Approximation for Efficient Large Vision-Language Models
by: Jha, Samyak, et al.
Published: (2026)
by: Jha, Samyak, et al.
Published: (2026)
ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning
by: Potamitis, Nearchos, et al.
Published: (2025)
by: Potamitis, Nearchos, et al.
Published: (2025)
Score-Guided Proximal Projection: A Unified Geometric Framework for Rectified Flow Editing
by: Bansal, Vansh, et al.
Published: (2026)
by: Bansal, Vansh, et al.
Published: (2026)
GCondenser: Benchmarking Graph Condensation
by: Liu, Yilun, et al.
Published: (2024)
by: Liu, Yilun, et al.
Published: (2024)
A Survey on Graph Condensation
by: Xu, Hongjia, et al.
Published: (2024)
by: Xu, Hongjia, et al.
Published: (2024)
Graph Condensation for Open-World Graph Learning
by: Gao, Xinyi, et al.
Published: (2024)
by: Gao, Xinyi, et al.
Published: (2024)
TextAge: A Curated and Diverse Text Dataset for Age Classification
by: Cheekati, Shravan, et al.
Published: (2024)
by: Cheekati, Shravan, et al.
Published: (2024)
Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning
by: Beigzad, Salar, et al.
Published: (2026)
by: Beigzad, Salar, et al.
Published: (2026)
Graph Neural Networks for Predicting Solubility in Diverse Solvents using MolMerger incorporating Solute-solvent Interactions
by: Ramani, Vansh, et al.
Published: (2024)
by: Ramani, Vansh, et al.
Published: (2024)
Dynamic Graph Condensation
by: Chen, Dong, et al.
Published: (2025)
by: Chen, Dong, et al.
Published: (2025)
SiDGen: Structure-informed Diffusion for Generative modeling of Ligands for Proteins
by: Sanghvi, Samyak, et al.
Published: (2025)
by: Sanghvi, Samyak, et al.
Published: (2025)
Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential
by: Kadri, Hachem, et al.
Published: (2025)
by: Kadri, Hachem, et al.
Published: (2025)
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
by: Gao, Xinyi, et al.
Published: (2024)
by: Gao, Xinyi, et al.
Published: (2024)
PLGC: Pseudo-Labeled Graph Condensation
by: Nandy, Jay, et al.
Published: (2026)
by: Nandy, Jay, et al.
Published: (2026)
GRAMA: Adaptive Graph Autoregressive Moving Average Models
by: Eliasof, Moshe, et al.
Published: (2025)
by: Eliasof, Moshe, et al.
Published: (2025)
Position: The Need for Ultrafast Training
by: Hoang, Duc
Published: (2026)
by: Hoang, Duc
Published: (2026)
Similar Items
-
Bonsai: Gradient-free Graph Condensation for Node Classification
by: Gupta, Mridul, et al.
Published: (2024) -
Mirage: Model-Agnostic Graph Distillation for Graph Classification
by: Gupta, Mridul, et al.
Published: (2023) -
On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026) -
Panorama: Fast-Track Nearest Neighbors
by: Ramani, Vansh, et al.
Published: (2025) -
GNNMerge: Merging of GNN Models Without Accessing Training Data
by: Garg, Vipul, et al.
Published: (2025)