Cover Image

Generalization of Graph Neural Networks through the Lens of Homomorphism

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Shouheng, Kim, Dongwoo, Wang, Qing
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Local Vertex Colouring Graph Neural Networks
by: Li, Shouheng, et al.
Published: (2024)

Towards Bridging Generalization and Expressivity of Graph Neural Networks
by: Li, Shouheng, et al.
Published: (2024)

Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering
by: Li, Shouheng, et al.
Published: (2022)

Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
by: Park, MoonJeong, et al.
Published: (2026)

Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
by: Park, MoonJeong, et al.
Published: (2024)

Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
by: Gai, Jingchu, et al.
Published: (2025)

Understanding the Failure Modes of Transformers through the Lens of Graph Neural Networks
by: Lee, Hunjae
Published: (2025)

Homomorphism Counts for Graph Neural Networks: All About That Basis
by: Jin, Emily, et al.
Published: (2024)

Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
by: Qin, Zhenyue, et al.
Published: (2021)

Graph Generation with $K^2$-trees
by: Jang, Yunhui, et al.
Published: (2023)

Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
by: Heo, Jaeseung, et al.
Published: (2025)

Rethinking Graph Generalization through the Lens of Sharpness-Aware Minimization
by: Qiu, Yang, et al.
Published: (2026)

On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems
by: Gravina, Alessio, et al.
Published: (2024)

Asymmetric Learning for Spectral Graph Neural Networks
by: Liu, Fangbing, et al.
Published: (2024)

Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
by: Park, MoonJeong, et al.
Published: (2024)

Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
by: Campi, Francesco, et al.
Published: (2023)

EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
by: Heo, Jaeseung, et al.
Published: (2023)

Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?
by: Hevapathige, Asela, et al.
Published: (2023)

BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
by: Zheng, Amber Yijia, et al.
Published: (2024)

Enhancing Signed Graph Neural Networks through Curriculum-Based Training
by: Zhang, Zeyu, et al.
Published: (2023)

Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
by: Huang, Zheng, et al.
Published: (2024)

Homomorphism Counts as Structural Encodings for Graph Learning
by: Bao, Linus, et al.
Published: (2024)

Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
by: Fan, Shaohua, et al.
Published: (2021)

Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
by: Sun, Li, et al.
Published: (2024)

Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
by: Chen, Zhuomin, et al.
Published: (2024)

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
by: Kim, Suyeon, et al.
Published: (2025)

Space of Data through the Lens of Multilevel Graph
by: Caputo, Marco, et al.
Published: (2025)

PAGE: Parametric Generative Explainer for Graph Neural Network
by: Qiu, Yang, et al.
Published: (2024)

SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
by: Zhou, Ziang, et al.
Published: (2024)

Disentangling Hyperedges through the Lens of Category Theory
by: Lee, Yoonho, et al.
Published: (2025)

A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
by: Wang, Zhiyang, et al.
Published: (2024)

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
by: Rauchwerger, Levi, et al.
Published: (2024)

Generating Robust Counterfactual Witnesses for Graph Neural Networks
by: Qiu, Dazhuo, et al.
Published: (2024)

Graphs Unveiled: Graph Neural Networks and Graph Generation
by: Kovács, László, et al.
Published: (2024)

Posterior Label Smoothing for Node Classification
by: Heo, Jaeseung, et al.
Published: (2024)

Retrieval-Augmented Generation with Estimation of Source Reliability
by: Hwang, Jeongyeon, et al.
Published: (2024)

Enhancing Robustness of Graph Neural Networks through p-Laplacian
by: Sirohi, Anuj Kumar, et al.
Published: (2024)

Enhancing Robustness of Graph Neural Networks through p-Laplacian
by: Sirohi, Anuj Kumar, et al.
Published: (2025)

Generative-Contrastive Heterogeneous Graph Neural Network
by: Wang, Yu, et al.
Published: (2024)

Depth-Adaptive Graph Neural Networks via Learnable Bakry-'Emery Curvature
by: Hevapathige, Asela, et al.
Published: (2025)