Cover Image

Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Beddar-Wiesing, Silvia, D'Inverno, Giuseppe Alessio, Graziani, Caterina, Lachi, Veronica, Moallemy-Oureh, Alice, Scarselli, Franco, Thomas, Josephine Maria
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks
by: Beddar-Wiesing, Silvia, et al.
Published: (2025)

Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network
by: Moallemy-Oureh, Alice, et al.
Published: (2022)

Absolute Evaluation Measures for Machine Learning: A Survey
by: Beddar-Wiesing, Silvia, et al.
Published: (2025)

VC dimension of Graph Neural Networks with Pfaffian activation functions
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2024)

On the approximation capability of GNNs in node classification/regression tasks
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2021)

A topological description of loss surfaces based on Betti Numbers
by: Bucarelli, Maria Sofia, et al.
Published: (2024)

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2024)

Generalizing Weisfeiler-Lehman Kernels to Subgraphs
by: Kim, Dongkwan, et al.
Published: (2024)

Surrogate models for diffusion on graphs via sparse polynomials
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2025)

Product of Tensors and Description of Networks
by: Chiantini, Luca, et al.
Published: (2024)

RSVP: Beyond Weisfeiler Lehman Graph Isomorphism Test
by: Dutta, Sourav, et al.
Published: (2024)

Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
by: Zhang, Bohang, et al.
Published: (2024)

Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks
by: Chen, Jiawen, et al.
Published: (2026)

Edged Weisfeiler-Lehman Algorithm
by: Yue, Xiao, et al.
Published: (2025)

Weisfeiler and Lehman Go Categorical
by: Choi, Seongjin, et al.
Published: (2026)

Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
by: Kummer, Lorenz, et al.
Published: (2023)

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
by: Feng, Jiarui, et al.
Published: (2023)

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
by: Longa, Antonio, et al.
Published: (2023)

From Token Lists to Graph Motifs: Weisfeiler-Lehman Analysis of Sparse Autoencoder Features
by: Fernandez-Boullon, Ruben, et al.
Published: (2026)

Physics-Informed GNN for non-linear constrained optimization: PINCO a solver for the AC-optimal power flow
by: Varbella, Anna, et al.
Published: (2024)

Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2024)

Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs
by: Heeg, Franziska, et al.
Published: (2025)

Revisiting Deep Information Propagation: Fractal Frontier and Finite-size Effects
by: D'Inverno, Giuseppe Alessio, et al.
Published: (2025)

Data-driven multi-agent modelling of calcium interactions in cell culture: PINN vs Regularized Least-squares
by: Poggi, Aurora, et al.
Published: (2025)

An innovative benefit‐of‐the‐doubt approach for health system effectiveness: a global case study on amenable mortality
by: Giovanna D'Inverno, et al.
Published: (2024)

Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
by: Perez, Raphaël Carpintero, et al.
Published: (2024)

Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
by: Truong, Quang, et al.
Published: (2023)

On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters
by: Lanzinger, Matthias, et al.
Published: (2023)

On Task Vectors and Gradients
by: Zhou, Luca, et al.
Published: (2025)

Chapter Digital Shogun and Electronic Imperialism: Japanese History through the Lens of Historical Videogames
by: Scarselli, Aldo Giuseppe
Published: (2024)

Weisfeiler‐Lehman Kernel Augmented Product Representation for Queries on Large‐Scale BIM Scenes
by: Huiqiang Hu, et al.
Published: (2025)

How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?
by: Cui, Guanyu, et al.
Published: (2024)

Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective
by: Zhou, Junru, et al.
Published: (2024)

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

Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power
by: Wang, Zhe, et al.
Published: (2024)

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction
by: Ferrini, Francesco, et al.
Published: (2025)

Graph Persistence goes Spectral
by: Ji, Mattie, et al.
Published: (2025)

HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness
by: Tian, Chengyu, et al.
Published: (2025)

Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignment
by: Naddeo, Dionisia, et al.
Published: (2026)

On the Expressive Power of Graph Neural Networks
by: Nalwade, Ashwin, et al.
Published: (2024)