HoloNets: Spectral Convolutions do extend to Directed Graphs

3 October 2023

Papers citing "HoloNets: Spectral Convolutions do extend to Directed Graphs"

9 / 9 papers shown

Title
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach Xunkai Li Zhengyu Wu Kaichi Yu Hongchao Qin Guang Zeng Rong-Hua Li Guoren Wang 35 0 0 02 May 2025
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding Thomas Dagès Simon Weber Y. Lin Ronen Talmon Daniel Cremers M. Lindenbaum A. Bruckstein Ron Kimmel 39 0 0 23 Mar 2025
A Benchmark on Directed Graph Representation Learning in Hardware Designs Haoyu Wang Yinan Huang Nan Wu Pan Li OOD 48 1 0 09 Oct 2024
Commute Graph Neural Networks Wei Zhuo Han Yu Guang Tan Xiaoxiao Li GNN 98 1 0 30 Jun 2024
Specformer: Spectral Graph Neural Networks Meet Transformers Deyu Bo Chuan Shi Lele Wang Renjie Liao 76 80 0 02 Mar 2023
Gradient Gating for Deep Multi-Rate Learning on Graphs T. Konstantin Rusch B. Chamberlain Michael W. Mahoney Michael M. Bronstein Siddhartha Mishra 79 53 0 02 Oct 2022
How Powerful are Spectral Graph Neural Networks Xiyuan Wang Muhan Zhang 80 181 0 23 May 2022
Transferability of Graph Neural Networks: an Extended Graphon Approach Sohir Maskey Ron Levie Gitta Kutyniok GNN 93 50 0 21 Sep 2021
Geom-GCN: Geometric Graph Convolutional Networks Hongbin Pei Bingzhen Wei Kevin Chen-Chuan Chang Yu Lei Bo Yang GNN 169 1,080 0 13 Feb 2020