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Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN
  Expressiveness

Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness

16 January 2024
Bohang Zhang
Jingchu Gai
Yiheng Du
Qiwei Ye
Di He
Liwei Wang
ArXivPDFHTML

Papers citing "Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness"

4 / 4 papers shown
Title
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
M. Lecha
Andrea Cavallo
Francesca Dominici
Elvin Isufi
Claudio Battiloro
AI4CE
151
2
0
17 Jan 2025
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Billy Joe Franks
Moshe Eliasof
Semih Cantürk
Guy Wolf
Carola-Bibiane Schönlieb
Sophie Fellenz
Marius Kloft
AI4CE
76
0
0
10 Dec 2024
Revisiting Random Walks for Learning on Graphs
Revisiting Random Walks for Learning on Graphs
Jinwoo Kim
Olga Zaghen
Ayhan Suleymanzade
Youngmin Ryou
Seunghoon Hong
59
0
0
01 Jul 2024
The Expressive Power of Graph Neural Networks: A Survey
The Expressive Power of Graph Neural Networks: A Survey
Bingxue Zhang
Changjun Fan
Shixuan Liu
Kuihua Huang
Xiang Zhao
Jin-Yu Huang
Zhong Liu
40
19
0
16 Aug 2023
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