Graph convolutions that can finally model local structure

Graph convolutions that can finally model local structure

30 November 2020

Papers citing "Graph convolutions that can finally model local structure"

11 / 11 papers shown

Title
Sampling Foundational Transformer: A Theoretical Perspective Viet Anh Nguyen Minh Lenhat Khoa Nguyen Duong Duc Hieu Dao Huu Hung Truong Son-Hy 42 0 0 11 Aug 2024
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning Tianlang Chen Shengjie Luo Di He Shuxin Zheng Tie-Yan Liu Liwei Wang AI4CE 31 5 0 24 Jun 2024
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers Md Shamim Hussain Mohammed J. Zaki D. Subramanian ViT 19 4 0 07 Feb 2024
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
Equilibrium Aggregation: Encoding Sets via Optimization Sergey Bartunov F. Fuchs Timothy Lillicrap 9 7 0 25 Feb 2022
GRPE: Relative Positional Encoding for Graph Transformer Wonpyo Park Woonggi Chang Donggeon Lee Juntae Kim Seung-won Hwang 39 74 0 30 Jan 2022
Global Self-Attention as a Replacement for Graph Convolution Md Shamim Hussain Mohammed J. Zaki D. Subramanian ViT 19 122 0 07 Aug 2021
Do Transformers Really Perform Bad for Graph Representation? Chengxuan Ying Tianle Cai Shengjie Luo Shuxin Zheng Guolin Ke Di He Yanming Shen Tie-Yan Liu GNN 21 431 0 09 Jun 2021
Autobahn: Automorphism-based Graph Neural Nets Erik H. Thiede Wenda Zhou Risi Kondor GNN AI4CE 16 48 0 02 Mar 2021
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 219 1,329 0 12 Feb 2018
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 159 1,766 0 02 Mar 2017