Cover Image

Towards Metric-Faithful Neural Graph Matching

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shivottam, Jyotirmaya, Mishra, Subhankar
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

QGShap: Quantum Acceleration for Faithful GNN Explanations
by: Jena, Haribandhu, et al.
Published: (2025)

QGraphLIME - Explaining Quantum Graph Neural Networks
by: Jena, Haribandhu, et al.
Published: (2025)

Graph Reconstruction from Differentially Private GNN Explanations
by: Sahoo, Rishi Raj, et al.
Published: (2026)

mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks
by: Mishra, Subhankar
Published: (2026)

Graph Neural Networks at a Fraction
by: Joshi, Rucha Bhalchandra, et al.
Published: (2025)

Faithful Interpretation for Graph Neural Networks
by: Hu, Lijie, et al.
Published: (2024)

Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method
by: Vishwakarma, Rahul, et al.
Published: (2023)

Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
by: Liu, Fanzhen, et al.
Published: (2025)

Transformer Circuit Faithfulness Metrics are not Robust
by: Miller, Joseph, et al.
Published: (2024)

Incorporating Attribution Importance for Improving Faithfulness Metrics
by: Zhao, Zhixue, et al.
Published: (2023)

Neural Graph Navigation for Intelligent Subgraph Matching
by: Ying, Yuchen, et al.
Published: (2025)

Generalized Tree Edit Distance (GTED): A Faithful Evaluation Metric for Statement Autoformalization
by: Liu, Yuntian, et al.
Published: (2025)

FaithLM: Towards Faithful Explanations for Large Language Models
by: Chuang, Yu-Neng, et al.
Published: (2024)

A Causal Lens for Evaluating Faithfulness Metrics
by: Zaman, Kerem, et al.
Published: (2025)

Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery
by: Yue, Linan, et al.
Published: (2024)

Approximate Subgraph Matching with Neural Graph Representations and Reinforcement Learning
by: Li, Kaiyang, et al.
Published: (2026)

Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
by: Downer, Jane, et al.
Published: (2024)

Towards Neural Scaling Laws on Graphs
by: Liu, Jingzhe, et al.
Published: (2024)

Towards Interpretability Without Sacrifice: Faithful Dense Layer Decomposition with Mixture of Decoders
by: Oldfield, James, et al.
Published: (2025)

FaithfulSAE: Towards Capturing Faithful Features with Sparse Autoencoders without External Dataset Dependencies
by: Cho, Seonglae, et al.
Published: (2025)

Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce
by: Ji, Houye, et al.
Published: (2024)

A Riemannian Approach to Ground Metric Learning for Optimal Transport
by: Jawanpuria, Pratik, et al.
Published: (2024)

Faithful or Just Plausible? Evaluating the Faithfulness of Closed-Source LLMs in Medical Reasoning
by: Afolabi, Halimat, et al.
Published: (2026)

Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
by: Wang, Yuwen, et al.
Published: (2024)

GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
by: Amara, Kenza, et al.
Published: (2022)

Schreier-Coset Graph Propagation
by: Mishra, Aryan, et al.
Published: (2025)

Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks
by: Zhang, Han, et al.
Published: (2025)

Towards Few-shot Self-explaining Graph Neural Networks
by: Peng, Jingyu, et al.
Published: (2024)

Neural Networks Use Distance Metrics
by: Oursland, Alan
Published: (2024)

Towards Unsupervised Training of Matching-based Graph Edit Distance Solver via Preference-aware GAN
by: Huang, Wei, et al.
Published: (2025)

A Metric for the Balance of Information in Graph Learning
by: Davies, Alex O., et al.
Published: (2025)

Metric Based Few-Shot Graph Classification
by: Crisostomi, Donato, et al.
Published: (2022)

Structural Invariance Matters: Rethinking Graph Rewiring through Graph Metrics
by: Benoit, Alexandre, et al.
Published: (2025)

Towards an Improved Metric for Evaluating Disentangled Representations
by: Julka, Sahib, et al.
Published: (2024)

Towards Adversarially Robust Deep Metric Learning
by: Ke, Xiaopeng
Published: (2025)

DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations
by: Guo, Yuhan, et al.
Published: (2025)

Towards Generalization of Graph Neural Networks for AC Optimal Power Flow
by: Arowolo, Olayiwola, et al.
Published: (2025)

Towards Scalable and Deep Graph Neural Networks via Noise Masking
by: Liang, Yuxuan, et al.
Published: (2024)

Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs
by: Rottoli, Michael, et al.
Published: (2026)

HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
by: Sang, Shilong, et al.
Published: (2025)