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2502.02488
Cited By
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?
4 February 2025
Xiang Wang
Wenshu Fan
Lexi Pang
Siwei Chen
Muhan Zhang
DiffM
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Papers citing
"Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?"
16 / 16 papers shown
Permutation-Invariant Spectral Learning via Dyson Diffusion
Tassilo Schwarz
Cai Dieball
Constantin Kogler
Kevin Lam
Renaud Lambiotte
Arnaud Doucet
Aljaž Godec
George Deligiannidis
DiffM
207
0
0
09 Oct 2025
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
Stratis Limnios
Praveen Selvaraj
Mihai Cucuringu
Carsten Maple
Gesine Reinert
Andrew Elliott
DiffM
319
13
0
29 Jun 2023
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
International Conference on Machine Learning (ICML), 2023
Cai Zhou
Xiyuan Wang
Muhan Zhang
287
22
0
08 May 2023
Equivariant Polynomials for Graph Neural Networks
International Conference on Machine Learning (ICML), 2023
Omri Puny
Derek Lim
B. Kiani
Haggai Maron
Y. Lipman
372
41
0
22 Feb 2023
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
International Conference on Machine Learning (ICML), 2023
Bohang Zhang
Guhao Feng
Yiheng Du
Di He
Liwei Wang
333
83
0
14 Feb 2023
Graph Generation with Diffusion Mixture
International Conference on Machine Learning (ICML), 2023
Jaehyeong Jo
Dongki Kim
Sung Ju Hwang
DiffM
497
46
0
07 Feb 2023
Diffusion Models for Graphs Benefit From Discrete State Spaces
K. Haefeli
Karolis Martinkus
Nathanael Perraudin
Roger Wattenhofer
DiffM
502
73
0
04 Oct 2022
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
International Conference on Learning Representations (ICLR), 2022
Sitan Chen
Sinho Chewi
Jungshian Li
Yuanzhi Li
Adil Salim
Anru R. Zhang
DiffM
779
406
0
22 Sep 2022
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
International Conference on Machine Learning (ICML), 2022
Jaehyeong Jo
Seul Lee
Sung Ju Hwang
DiffM
469
323
0
05 Feb 2022
Nested Graph Neural Networks
Neural Information Processing Systems (NeurIPS), 2021
Muhan Zhang
Pan Li
364
204
0
25 Oct 2021
Permutation Invariant Graph Generation via Score-Based Generative Modeling
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Chenhao Niu
Yang Song
Jiaming Song
Shengjia Zhao
Aditya Grover
Stefano Ermon
DiffM
326
350
0
02 Mar 2020
Provably Powerful Graph Networks
Neural Information Processing Systems (NeurIPS), 2019
Haggai Maron
Heli Ben-Hamu
Hadar Serviansky
Y. Lipman
716
659
0
27 May 2019
Invariant and Equivariant Graph Networks
Haggai Maron
Heli Ben-Hamu
Nadav Shamir
Y. Lipman
497
555
0
24 Dec 2018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher Morris
Martin Ritzert
Matthias Fey
William L. Hamilton
J. E. Lenssen
Gaurav Rattan
Martin Grohe
GNN
917
1,943
0
04 Oct 2018
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
1.2K
9,531
0
01 Oct 2018
Neural Message Passing for Quantum Chemistry
Justin Gilmer
S. Schoenholz
Patrick F. Riley
Oriol Vinyals
George E. Dahl
1.6K
8,789
0
04 Apr 2017
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