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NEAR: Neighborhood Edge AggregatoR for Graph Classification
v1v2 (latest)

NEAR: Neighborhood Edge AggregatoR for Graph Classification

6 September 2019
Cheolhyeong Kim
Haeseong Moon
H. Hwang
    GNN
ArXiv (abs)PDFHTML

Papers citing "NEAR: Neighborhood Edge AggregatoR for Graph Classification"

29 / 29 papers shown
Title
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph
  Representations
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Ekagra Ranjan
Soumya Sanyal
Partha P. Talukdar
GNN
174
334
0
18 Nov 2019
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and
  Convolutional-LSTM Network
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Md. Rezaul Karim
Michael Cochez
J. Jares
Mamtaz Uddin
Oya Beyan
Stefan Decker
55
126
0
04 Aug 2019
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph
  Kernels
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
S. Du
Kangcheng Hou
Barnabás Póczós
Ruslan Salakhutdinov
Ruosong Wang
Keyulu Xu
135
276
0
30 May 2019
Understanding Attention and Generalization in Graph Neural Networks
Understanding Attention and Generalization in Graph Neural Networks
Boris Knyazev
Graham W. Taylor
Mohamed R. Amer
GNN
147
340
0
08 May 2019
Self-Attention Graph Pooling
Self-Attention Graph Pooling
Junhyun Lee
Inyeop Lee
Jaewoo Kang
GNN
175
1,121
0
17 Apr 2019
Knowledge Graph Convolutional Networks for Recommender Systems
Knowledge Graph Convolutional Networks for Recommender Systems
Hongwei Wang
Miao Zhao
Xing Xie
Wenjie Li
Minyi Guo
71
931
0
18 Mar 2019
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
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
192
1,636
0
04 Oct 2018
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
243
7,653
0
01 Oct 2018
RetGK: Graph Kernels based on Return Probabilities of Random Walks
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Zhen Zhang
Mianzhi Wang
Yijian Xiang
Yuanmin Huang
A. Nehorai
98
106
0
07 Sep 2018
Hierarchical Graph Representation Learning with Differentiable Pooling
Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying
Jiaxuan You
Christopher Morris
Xiang Ren
William L. Hamilton
J. Leskovec
GNN
297
2,148
0
22 Jun 2018
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
513
1,982
0
09 Jun 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph
  Generation
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You
Bowen Liu
Rex Ying
Vijay S. Pande
J. Leskovec
GNN
293
902
0
07 Jun 2018
Embedding Logical Queries on Knowledge Graphs
Embedding Logical Queries on Knowledge Graphs
William L. Hamilton
Payal Bajaj
Marinka Zitnik
Dan Jurafsky
J. Leskovec
NAI
80
290
0
05 Jun 2018
MolGAN: An implicit generative model for small molecular graphs
MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao
Thomas Kipf
GNNGAN
177
926
0
30 May 2018
Anonymous Walk Embeddings
Anonymous Walk Embeddings
Sergey Ivanov
Evgeny Burnaev
GNN
59
179
0
30 May 2018
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal
  Graphs
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
Jiani Zhang
Xingjian Shi
Junyuan Xie
Hao Ma
Irwin King
Dit-Yan Yeung
GNN
110
573
0
20 Mar 2018
SimplE Embedding for Link Prediction in Knowledge Graphs
SimplE Embedding for Link Prediction in Knowledge Graphs
Seyed Mehran Kazemi
David Poole
76
716
0
13 Feb 2018
Graph Attention Networks
Graph Attention Networks
Petar Velickovic
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio
Yoshua Bengio
GNN
479
20,164
0
30 Oct 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
713
131,652
0
12 Jun 2017
Inductive Representation Learning on Large Graphs
Inductive Representation Learning on Large Graphs
William L. Hamilton
Z. Ying
J. Leskovec
509
15,247
0
07 Jun 2017
Neural Message Passing for Quantum Chemistry
Neural Message Passing for Quantum Chemistry
Justin Gilmer
S. Schoenholz
Patrick F. Riley
Oriol Vinyals
George E. Dahl
593
7,455
0
04 Apr 2017
MoleculeNet: A Benchmark for Molecular Machine Learning
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
337
1,827
0
02 Mar 2017
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
412
1,823
0
25 Nov 2016
Variational Graph Auto-Encoders
Variational Graph Auto-Encoders
Thomas Kipf
Max Welling
GNNBDLSSLCML
151
3,586
0
21 Nov 2016
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNNSSL
644
29,076
0
09 Sep 2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral
  Filtering
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
M. Defferrard
Xavier Bresson
P. Vandergheynst
GNN
353
7,655
0
30 Jun 2016
Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for Graphs
Mathias Niepert
Mohamed Ahmed
Konstantin Kutzkov
GNNSSL
138
2,154
0
17 May 2016
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
463
43,305
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.9K
150,115
0
22 Dec 2014
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