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While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph.
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Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs
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A (sub)graph isomorphism algorithm for matching large graphs
Luigi P. Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento · 2004
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A faster algorithm for detecting network motifs
Sebastian Wernicke · 2005
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Sebastian Wernicke · 2006
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FANMOD: a tool for fast network motif detection
Sebastian Wernicke and Florian Rasche · 2006
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Nataša Pržulj · 2007
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Uncovering biological network function via graphlet degree signatures
Tijana Milenković and Nataša Pržulj · 2008
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Efficient graphlet kernels for large graph comparison
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Fast neighborhood subgraph pairwise distance kernel
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Subgraph matching kernels for attributed graphs
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ZINC: A free tool to discover chemistry for biology
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Convolutional networks on graphs for learning molecular fingerprints
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Geodesic convolutional neural networks on riemannian manifolds
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End-to-end memory networks
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Interaction networks for learning about objects, relations and physics
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Higher-order organization of complex networks
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Graphlet-based characterization of directed networks
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Propagation kernels: efficient graph kernels from propagated information
Marion Neumann, Roman Garnett, Christian Bauckhage, and Kristian Kersting · 2016
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Protein interface prediction using graph convolutional networks
Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur · 2017
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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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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bronstein · 2017
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Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Ashwin Paranjape, Austin R. Benson, and Jure Leskovec · 2017
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Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
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M. R. Dareddy, M. Das, and H. Yang · 2019
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Aravind Sankar, Xinyang Zhang, and Kevin Chen-Chuan Chang · 2019
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John Boaz Lee, Ryan Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao · 2019
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Michael Lingzhi Li, Meng Dong, Jiawei Zhou, and Alexander M Rush · 2019
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Martin Fürer · 2017
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Grammar variational autoencoder
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Xavier Bresson and Thomas Laurent · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Cheolhyeong Kim, Haeseong Moon, and Hyung Ju Hwang · 2019
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Simon S. Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabás Póczos, Ruosong Wang, and Keyulu Xu · 2019
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A persistent weisfeiler-lehman procedure for graph classification
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Fast graph representation learning with pytorch geometric
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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Gradient descent finds global minima of deep neural networks
Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2019
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