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Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph
  Neural Networks in Molecular Graph Analysis
v1v2v3v4 (latest)

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis

4 February 2019
Katsuhiko Ishiguro
S. Maeda
Masanori Koyama
    GNN
ArXiv (abs)PDFHTML

Papers citing "Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis"

17 / 17 papers shown
Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series
  Representation Learning
Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning
Sagar Srinivas Sakhinana
Krishna Sai Sudhir Aripirala
Shivam Gupta
Venkataramana Runkana
BDLAI4TSAI4CE
255
0
0
22 Aug 2024
Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes
Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes
Sakhinana Sagar Srinivas
Rajat Kumar Sarkar
Sreeja Gangasani
Venkataramana Runkana
391
2
0
21 Aug 2024
Equivariant Hypergraph Neural Networks
Equivariant Hypergraph Neural NetworksEuropean Conference on Computer Vision (ECCV), 2022
Jinwoo Kim
Saeyoon Oh
Sungjun Cho
Seunghoon Hong
207
16
0
22 Aug 2022
Embedding Graphs on Grassmann Manifold
Embedding Graphs on Grassmann ManifoldNeural Networks (NN), 2022
Bingxin Zhou
Xuebin Zheng
Yu Guang Wang
Ming Li
Junbin Gao
252
4
0
30 May 2022
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph
  Neural Network Inference
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network InferenceInternational Symposium on High-Performance Computer Architecture (HPCA), 2022
Rishov Sarkar
Stefan Abi-Karam
Yuqiang He
Lakshmi Sathidevi
Cong Hao
AI4CEGNN
302
52
0
27 Apr 2022
Enhanced compound-protein binding affinity prediction by representing
  protein multimodal information via a coevolutionary strategy
Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategybioRxiv (bioRxiv), 2022
Binjie Guo
Hanyu Zheng
Haohan Jiang
Xiaodan Li
Nai-Yang Guan
Yanming Zuo
Yicheng Zhang
Hengfu Yang
Xuhua Wang
190
5
0
30 Mar 2022
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
Stefan Abi-Karam
Yuqi He
Rishov Sarkar
Lakshmi Sathidevi
Zihang Qiao
Cong Hao
GNN
144
19
0
20 Jan 2022
Nested Graph Neural Networks
Nested Graph Neural NetworksNeural Information Processing Systems (NeurIPS), 2021
Muhan Zhang
Pan Li
364
204
0
25 Oct 2021
How Framelets Enhance Graph Neural Networks
How Framelets Enhance Graph Neural NetworksInternational Conference on Machine Learning (ICML), 2021
Xuebin Zheng
Bingxin Zhou
Junbin Gao
Yu Guang Wang
Pietro Lio
Ming Li
Guido Montúfar
359
83
0
13 Feb 2021
Subgraph Neural Networks
Subgraph Neural Networks
Emily Alsentzer
S. G. Finlayson
Michelle M. Li
Marinka Zitnik
GNN
433
170
0
18 Jun 2020
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
Katsuhiko Ishiguro
Kenta Oono
K. Hayashi
GNN
192
4
0
12 Jun 2020
How hard is to distinguish graphs with graph neural networks?
How hard is to distinguish graphs with graph neural networks?
Andreas Loukas
GNN
429
7
0
13 May 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Open Graph Benchmark: Datasets for Machine Learning on GraphsNeural Information Processing Systems (NeurIPS), 2020
Weihua Hu
Matthias Fey
Marinka Zitnik
Yuxiao Dong
Hongyu Ren
Bowen Liu
Michele Catasta
J. Leskovec
1.1K
3,465
0
02 May 2020
Molecule Attention Transformer
Molecule Attention Transformer
Lukasz Maziarka
Tomasz Danel
Slawomir Mucha
Krzysztof Rataj
Jacek Tabor
Stanislaw Jastrzebski
377
196
0
19 Feb 2020
Scalable Generative Models for Graphs with Graph Attention Mechanism
Scalable Generative Models for Graphs with Graph Attention Mechanism
Wataru Kawai
Yusuke Mukuta
Tatsuya Harada
GNN
173
19
0
05 Jun 2019
Strategies for Pre-training Graph Neural Networks
Strategies for Pre-training Graph Neural NetworksInternational Conference on Learning Representations (ICLR), 2019
Weihua Hu
Bowen Liu
Joseph Gomes
Marinka Zitnik
Abigail Z. Jacobs
Vijay S. Pande
J. Leskovec
SSLAI4CE
602
1,732
0
29 May 2019
Analyzing Learned Molecular Representations for Property Prediction
Analyzing Learned Molecular Representations for Property Prediction
Kevin Kaichuang Yang
Kyle Swanson
Wengong Jin
Connor W. Coley
Philipp Eiden
...
Andrew Palmer
Volker Settels
Tommi Jaakkola
K. Jensen
Regina Barzilay
481
1,629
0
02 Apr 2019
1
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