Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
Fuente:
arXiv
Saved in:
| Main Authors: | Feng, Jiarui, Kong, Lecheng, Liu, Hao, Tao, Dacheng, Li, Fuhai, Zhang, Muhan, Chen, Yixin |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
One for All: Towards Training One Graph Model for All Classification Tasks
by: Liu, Hao, et al.
Published: (2023)
by: Liu, Hao, et al.
Published: (2023)
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
by: Zhou, Junru, et al.
Published: (2023)
by: Zhou, Junru, et al.
Published: (2023)
Edged Weisfeiler-Lehman Algorithm
by: Yue, Xiao, et al.
Published: (2025)
by: Yue, Xiao, et al.
Published: (2025)
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
by: Kong, Lecheng, et al.
Published: (2024)
by: Kong, Lecheng, et al.
Published: (2024)
TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
by: Feng, Jiarui, et al.
Published: (2024)
by: Feng, Jiarui, et al.
Published: (2024)
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
by: Dong, Zehao, et al.
Published: (2023)
by: Dong, Zehao, et al.
Published: (2023)
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
by: Kummer, Lorenz, et al.
Published: (2023)
by: Kummer, Lorenz, et al.
Published: (2023)
Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks
by: Beddar-Wiesing, Silvia, et al.
Published: (2025)
by: Beddar-Wiesing, Silvia, et al.
Published: (2025)
Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks
by: Chen, Jiawen, et al.
Published: (2026)
by: Chen, Jiawen, et al.
Published: (2026)
Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
by: Kong, Lecheng, et al.
Published: (2025)
by: Kong, Lecheng, et al.
Published: (2025)
Weisfeiler and Lehman Go Categorical
by: Choi, Seongjin, et al.
Published: (2026)
by: Choi, Seongjin, et al.
Published: (2026)
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
by: Beddar-Wiesing, Silvia, et al.
Published: (2022)
by: Beddar-Wiesing, Silvia, et al.
Published: (2022)
Generalizing Weisfeiler-Lehman Kernels to Subgraphs
by: Kim, Dongkwan, et al.
Published: (2024)
by: Kim, Dongkwan, et al.
Published: (2024)
Large Language Models Meet Graph Neural Networks for Text-Numeric Graph Reasoning
by: Song, Haoran, et al.
Published: (2025)
by: Song, Haoran, et al.
Published: (2025)
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
by: Dong, Zehao, et al.
Published: (2023)
by: Dong, Zehao, et al.
Published: (2023)
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
by: Zhang, Bohang, et al.
Published: (2024)
by: Zhang, Bohang, et al.
Published: (2024)
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
by: Perez, Raphaël Carpintero, et al.
Published: (2024)
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)
by: Truong, Quang, et al.
Published: (2023)
How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?
by: Cui, Guanyu, et al.
Published: (2024)
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)
by: Zhou, Junru, et al.
Published: (2024)
RSVP: Beyond Weisfeiler Lehman Graph Isomorphism Test
by: Dutta, Sourav, et al.
Published: (2024)
by: Dutta, Sourav, et al.
Published: (2024)
GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model
by: Yang, Haotong, et al.
Published: (2024)
by: Yang, Haotong, et al.
Published: (2024)
GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
by: Zhang, Heming, et al.
Published: (2024)
by: Zhang, Heming, et al.
Published: (2024)
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
by: Dong, Zehao, et al.
Published: (2024)
by: Dong, Zehao, et al.
Published: (2024)
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)
by: Tian, Chengyu, et al.
Published: (2025)
Using Random Noise Equivariantly to Boost Graph Neural Networks Universally
by: Wang, Xiyuan, et al.
Published: (2025)
by: Wang, Xiyuan, et al.
Published: (2025)
GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
by: Feng, Jiarui, et al.
Published: (2025)
by: Feng, Jiarui, et al.
Published: (2025)
Topology-aware Embedding Memory for Continual Learning on Expanding Networks
by: Zhang, Xikun, et al.
Published: (2024)
by: Zhang, Xikun, et al.
Published: (2024)
TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion
by: Cai, Donghong, et al.
Published: (2026)
by: Cai, Donghong, et al.
Published: (2026)
Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
by: Wang, Xiyuan, et al.
Published: (2023)
by: Wang, Xiyuan, 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)
by: Fernandez-Boullon, Ruben, et al.
Published: (2026)
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
by: Yang, Ling, et al.
Published: (2023)
by: Yang, Ling, et al.
Published: (2023)
On Lexical Invariance on Multisets and Graphs
by: Zhang, Muhan
Published: (2024)
by: Zhang, Muhan
Published: (2024)
Rethinking Structure Learning For Graph Neural Networks
by: Zheng, Yilun, et al.
Published: (2024)
by: Zheng, Yilun, et al.
Published: (2024)
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
by: Chen, Ziang, et al.
Published: (2024)
by: Chen, Ziang, et al.
Published: (2024)
Weisfeiler‐Lehman Kernel Augmented Product Representation for Queries on Large‐Scale BIM Scenes
by: Huiqiang Hu, et al.
Published: (2025)
by: Huiqiang Hu, et al.
Published: (2025)
On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters
by: Lanzinger, Matthias, et al.
Published: (2023)
by: Lanzinger, Matthias, et al.
Published: (2023)
Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization
by: Shen, Li, et al.
Published: (2026)
by: Shen, Li, et al.
Published: (2026)
Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
by: Liu, Yewei, et al.
Published: (2025)
by: Liu, Yewei, et al.
Published: (2025)
Graph Neural Networks for Graphs with Heterophily: A Survey
by: Zheng, Xin, et al.
Published: (2022)
by: Zheng, Xin, et al.
Published: (2022)
Similar Items
-
One for All: Towards Training One Graph Model for All Classification Tasks
by: Liu, Hao, et al.
Published: (2023) -
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
by: Zhou, Junru, et al.
Published: (2023) -
Edged Weisfeiler-Lehman Algorithm
by: Yue, Xiao, et al.
Published: (2025) -
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
by: Kong, Lecheng, et al.
Published: (2024) -
TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
by: Feng, Jiarui, et al.
Published: (2024)