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Message Detouring: A Simple Yet Effective Cycle Representation for
  Expressive Graph Learning

Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning

12 February 2024
Ziquan Wei
Tingting Dan
Guorong Wu
ArXivPDFHTML

Papers citing "Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning"

6 / 6 papers shown
Title
Cycle Invariant Positional Encoding for Graph Representation Learning
Cycle Invariant Positional Encoding for Graph Representation Learning
Zuoyu Yan
Teng Ma
Liangcai Gao
Zhi Tang
Chao Chen
Yusu Wang
20
4
0
24 Nov 2023
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
Geom-GCN: Geometric Graph Convolutional Networks
Geom-GCN: Geometric Graph Convolutional Networks
Hongbin Pei
Bingzhen Wei
Kevin Chen-Chuan Chang
Yu Lei
Bo Yang
GNN
167
1,058
0
13 Feb 2020
Multi-scale Attributed Node Embedding
Multi-scale Attributed Node Embedding
Benedek Rozemberczki
Carl Allen
Rik Sarkar
GNN
145
828
0
28 Sep 2019
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
211
1,329
0
12 Feb 2018
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
231
3,202
0
24 Nov 2016
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