Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | He, Yuntian, Maneriker, Pranav, Srinivasan, Anutam, Vadlamani, Aditya T., Parthasarathy, Srinivasan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
by: Maneriker, Pranav, et al.
Published: (2024)
by: Maneriker, Pranav, et al.
Published: (2024)
A Generic Framework for Conformal Fairness
by: Vadlamani, Aditya T., et al.
Published: (2025)
by: Vadlamani, Aditya T., et al.
Published: (2025)
FedCF: Fair Federated Conformal Prediction
by: Srinivasan, Anutam, et al.
Published: (2025)
by: Srinivasan, Anutam, et al.
Published: (2025)
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
by: Zhang, Yue, et al.
Published: (2024)
by: Zhang, Yue, et al.
Published: (2024)
Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
by: Srinivasan, Anutam, et al.
Published: (2026)
by: Srinivasan, Anutam, et al.
Published: (2026)
Can we ease the Injectivity Bottleneck on Lorentzian Manifolds for Graph Neural Networks?
by: Srinivasan, Srinitish, et al.
Published: (2025)
by: Srinivasan, Srinitish, et al.
Published: (2025)
Spectral Neural Graph Sparsification
by: Liguori, Angelica, et al.
Published: (2025)
by: Liguori, Angelica, et al.
Published: (2025)
SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks
by: Das, Siddhartha Shankar, et al.
Published: (2025)
by: Das, Siddhartha Shankar, et al.
Published: (2025)
Benchmarking Long Roll-outs of Auto-regressive Neural Operators for the Compressible Navier-Stokes Equations with Conserved Quantity Correction
by: Current, Sean, et al.
Published: (2026)
by: Current, Sean, et al.
Published: (2026)
Similarity-Navigated Conformal Prediction for Graph Neural Networks
by: Song, Jianqing, et al.
Published: (2024)
by: Song, Jianqing, et al.
Published: (2024)
Conformalized Link Prediction on Graph Neural Networks
by: Zhao, Tianyi, et al.
Published: (2024)
by: Zhao, Tianyi, et al.
Published: (2024)
Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
by: Liang, Langzhang, et al.
Published: (2025)
by: Liang, Langzhang, et al.
Published: (2025)
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
by: Xie, Beini, et al.
Published: (2024)
by: Xie, Beini, et al.
Published: (2024)
Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
by: Krishnagopal, Sanjukta
Published: (2026)
by: Krishnagopal, Sanjukta
Published: (2026)
Predict, Cluster, Refine: A Joint Embedding Predictive Self-Supervised Framework for Graph Representation Learning
by: Srinivasan, Srinitish, et al.
Published: (2025)
by: Srinivasan, Srinitish, et al.
Published: (2025)
Residual Reweighted Conformal Prediction for Graph Neural Networks
by: Zhang, Zheng, et al.
Published: (2025)
by: Zhang, Zheng, et al.
Published: (2025)
Less Is More -- On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP
by: Lischka, Attila, et al.
Published: (2024)
by: Lischka, Attila, et al.
Published: (2024)
Conformal Inductive Graph Neural Networks
by: Zargarbashi, Soroush H., et al.
Published: (2024)
by: Zargarbashi, Soroush H., et al.
Published: (2024)
Graph Sparsification via Mixture of Graphs
by: Zhang, Guibin, et al.
Published: (2024)
by: Zhang, Guibin, et al.
Published: (2024)
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information
by: Akgül, Ömer Faruk, et al.
Published: (2024)
by: Akgül, Ömer Faruk, et al.
Published: (2024)
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report
by: Ding, Haipeng, et al.
Published: (2025)
by: Ding, Haipeng, et al.
Published: (2025)
GENIE: Watermarking Graph Neural Networks for Link Prediction
by: Bachina, Venkata Sai Pranav, et al.
Published: (2024)
by: Bachina, Venkata Sai Pranav, et al.
Published: (2024)
MedEqualizer: A Framework Investigating Bias in Synthetic Medical Data and Mitigation via Augmentation
by: Salarian, Sama, et al.
Published: (2025)
by: Salarian, Sama, et al.
Published: (2025)
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
by: Baghershahi, Peyman, et al.
Published: (2025)
by: Baghershahi, Peyman, et al.
Published: (2025)
Online Sparsification of Bipartite-Like Clusters in Graphs
by: Das, Joyentanuj, et al.
Published: (2025)
by: Das, Joyentanuj, et al.
Published: (2025)
Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification
by: Sun, Jiawei, et al.
Published: (2025)
by: Sun, Jiawei, et al.
Published: (2025)
Conditional Shift-Robust Conformal Prediction for Graph Neural Network
by: Akansha, S.
Published: (2024)
by: Akansha, S.
Published: (2024)
Extending Temporal Disturbance Estimations For Magnetic Anomaly Navigation and Mapping
by: Srinivasan, Anutam, et al.
Published: (2025)
by: Srinivasan, Anutam, et al.
Published: (2025)
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
by: Wang, Ting, et al.
Published: (2025)
by: Wang, Ting, et al.
Published: (2025)
Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks
by: Wang, Tuo, et al.
Published: (2025)
by: Wang, Tuo, et al.
Published: (2025)
Scaling Equilibrium Propagation to Deeper Neural Network Architectures
by: Elayedam, Sankar Vinayak, et al.
Published: (2025)
by: Elayedam, Sankar Vinayak, et al.
Published: (2025)
Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophily
by: Lu, Kangkang, et al.
Published: (2025)
by: Lu, Kangkang, et al.
Published: (2025)
Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers
by: Kohli, Harsh, et al.
Published: (2026)
by: Kohli, Harsh, et al.
Published: (2026)
Empirical Error Estimates for Graph Sparsification
by: Wang, Siyao, et al.
Published: (2025)
by: Wang, Siyao, et al.
Published: (2025)
Extracting Usable Predictions from Quantized Networks through Uncertainty Quantification for OOD Detection
by: Singhal, Rishi, et al.
Published: (2024)
by: Singhal, Rishi, et al.
Published: (2024)
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification
by: Akansha, S.
Published: (2024)
by: Akansha, S.
Published: (2024)
Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs
by: He, Shuqi, et al.
Published: (2024)
by: He, Shuqi, et al.
Published: (2024)
Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
by: Regier, Derek, et al.
Published: (2026)
by: Regier, Derek, et al.
Published: (2026)
Energy-efficient Decentralized Learning via Graph Sparsification
by: Zhang, Xusheng, et al.
Published: (2024)
by: Zhang, Xusheng, 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)
Similar Items
-
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
by: Maneriker, Pranav, et al.
Published: (2024) -
A Generic Framework for Conformal Fairness
by: Vadlamani, Aditya T., et al.
Published: (2025) -
FedCF: Fair Federated Conformal Prediction
by: Srinivasan, Anutam, et al.
Published: (2025) -
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
by: Zhang, Yue, et al.
Published: (2024) -
Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis
by: Srinivasan, Anutam, et al.
Published: (2026)