ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2407.03125
  4. Cited By
Foundations and Frontiers of Graph Learning Theory

Foundations and Frontiers of Graph Learning Theory

3 July 2024
Yu Huang
Min Zhou
Menglin Yang
Zhen Wang
Muhan Zhang
Jie Wang
Hong Xie
Hao Wang
Defu Lian
Enhong Chen
    AI4CE
    GNN
ArXivPDFHTML

Papers citing "Foundations and Frontiers of Graph Learning Theory"

17 / 17 papers shown
Title
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN
  Expressiveness
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
Bohang Zhang
Jingchu Gai
Yiheng Du
Qiwei Ye
Di He
Liwei Wang
41
33
0
16 Jan 2024
MATA*: Combining Learnable Node Matching with A* Algorithm for
  Approximate Graph Edit Distance Computation
MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation
Junfeng Liu
Min Zhou
Shuai Ma
Lujia Pan
19
4
0
04 Nov 2023
Mitigating Over-Smoothing and Over-Squashing using Augmentations of
  Forman-Ricci Curvature
Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature
Lukas Fesser
Melanie Weber
56
8
0
17 Sep 2023
The Descriptive Complexity of Graph Neural Networks
The Descriptive Complexity of Graph Neural Networks
Martin Grohe
GNN
28
22
0
08 Mar 2023
Transposed Variational Auto-encoder with Intrinsic Feature Learning for
  Traffic Forecasting
Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting
Leyan Deng
Chenwang Wu
Defu Lian
Mintao Zhou
17
4
0
30 Oct 2022
Boosting the Cycle Counting Power of Graph Neural Networks with
  I$^2$-GNNs
Boosting the Cycle Counting Power of Graph Neural Networks with I2^22-GNNs
Yinan Huang
Xingang Peng
Jianzhu Ma
Muhan Zhang
76
46
0
22 Oct 2022
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of
  Graph Neural Networks for Attributed and Dynamic Graphs
Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs
Silvia Beddar-Wiesing
Giuseppe Alessio D’Inverno
C. Graziani
Veronica Lachi
Alice Moallemy-Oureh
F. Scarselli
J. M. Thomas
17
9
0
08 Oct 2022
Expander Graph Propagation
Expander Graph Propagation
Andreea Deac
Marc Lackenby
Petar Velivcković
93
41
0
06 Oct 2022
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs
  in Larger Test Graphs
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
Yangze Zhou
Gitta Kutyniok
Bruno Ribeiro
OODD
AI4CE
70
37
0
30 May 2022
How Powerful are Spectral Graph Neural Networks
How Powerful are Spectral Graph Neural Networks
Xiyuan Wang
Muhan Zhang
70
175
0
23 May 2022
Message passing all the way up
Message passing all the way up
Petar Velickovic
106
63
0
22 Feb 2022
A Theoretical Comparison of Graph Neural Network Extensions
A Theoretical Comparison of Graph Neural Network Extensions
Pál András Papp
Roger Wattenhofer
89
45
0
30 Jan 2022
Graph Neural Networks with Learnable Structural and Positional
  Representations
Graph Neural Networks with Learnable Structural and Positional Representations
Vijay Prakash Dwivedi
A. Luu
T. Laurent
Yoshua Bengio
Xavier Bresson
GNN
179
304
0
15 Oct 2021
Reconstruction for Powerful Graph Representations
Reconstruction for Powerful Graph Representations
Leonardo Cotta
Christopher Morris
Bruno Ribeiro
AI4CE
117
78
0
01 Oct 2021
Beyond Low-frequency Information in Graph Convolutional Networks
Beyond Low-frequency Information in Graph Convolutional Networks
Deyu Bo
Xiao Wang
C. Shi
Huawei Shen
GNN
84
445
0
04 Jan 2021
Benchmarking Graph Neural Networks
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Anh Tuan Luu
T. Laurent
Yoshua Bengio
Xavier Bresson
173
907
0
02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
217
1,726
0
09 Jun 2018
1