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We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph.
Processing directed acyclic graphs with recursive neural networks
Monica. Bianchini, Maria Cristina Gori, and Franco Scarselli · 2001
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D. Dobson and Andrew J. Doig · 2003
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Protein function prediction via graph kernels
Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alex J. Smola, and Hans-Peter Kriegel · 2005
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Weighted graph cuts without eigenvectors a multilevel approach
Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis · 2007
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Fundamentals of convex analysis
Jean-Baptiste Hiriart-Urruty and Claude Lemarechal · 2012
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Kron reduction of graphs with applications to electrical networks
Florian Dörfler and Francesco Bullo · 2013
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Scalable kernels for graphs with continuous attributes
Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, and Karsten Borgwardt · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Advanced coarsening schemes for graph partitioning
Ilya Safro, Peter Sanders, and Christian Schulz · 2014
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Sequential information maximization: When is greedy near-optimal?
Yuxin Chen, S. Hamed Hassani, Amin Karbasi, and Andreas Krause · 2015
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Adam: A method for stochastic optimization
Jimmy Ba Diederik P. Kingma · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
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Unet: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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A structural smoothing framework for robust graph comparison
Pinar Yanardag and S.V. N. Vishwanathan · 2015
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2016
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michael Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Variational graph auto-encoders
Thomas Kipf and Max Welling · 2016
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkovl · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Learning topological representation for networks via hierarchical sampling
Guoji Fu, Chengbin Hou, and Xin Yao · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Dynamic multi-scale filters for semantic segmentation
Junjun He, Zhongying Deng, and Yu Qiao · 2019
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X-cnn: Cross-modal convolutional neural networks for sparse datasets
Petar Veličković, Duo Wang, Nicholas D. Lane, and Pietro Liò · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas Kipf and Max Welling · 2017
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Deriving neural architectures from sequence and graph kernels
Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2017
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Attpool: Towards hierarchical feature representation in graph convolutional networks via attention mechanism
Jingjia Huang, Zhangheng Li, Nannan Li, Shan Liu, and Ge Li · 2019
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Self-attention graph pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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Actional-structural graph convolutional networks for skeleton-based action recognition
Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, and Qi Tian · 2019
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Lanczosnet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard Zemel · 2019
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Deep high-resolution representation learning for human pose estimation
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang · 2019
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Deep graph infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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Graphzoom: A multi-level spectral approach for accurate and scalable graph embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, and Zhuo Feng · 2020
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A fair comparison of graph neural networks for graph classification
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang · 2020
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Structpool: Structured graph pooling via conditional random fields
Hao Yuan and Shuiwang Ji · 2020
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