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There has been a surge of recent interest in learning representations for graph-structured data.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 1905
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Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
Joseph B Kruskal · 1964
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A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
Luis B Almeida · 1987
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Generalization of back propagation to recurrent and higher order neural networks
Fernando J Pineda · 1988
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Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, Rosa L. Lopez de Compadre, Gargi Debnath, Alan J. Shusterman, , and Corwin Hansch · 1991
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford · 2000
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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Learning from labeled and unlabeled data with label propagation
Xiaojin Zhu and Zoubin Ghahramani · 2002
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Semi-supervised learning on riemannian manifolds
Mikhail Belkin and Partha Niyogi · 2004
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Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf · 2004
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
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Biogrid: a general repository for interaction datasets
Chris Stark, Bobby-Joe Breitkreutz, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, and Mike Tyers · 2006
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Graph clustering with graph neural networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi, and Emmanuel Müller · 2006
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Geographic routing using hyperbolic space
Robert Kleinberg · 2007
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The link-prediction problem for social networks
David Liben-Nowell and Jon Kleinberg · 2007
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Multidimensional scaling
Michael AA Cox and Trevor F Cox · 2008
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Mapreduce: Simplified data processing on large clusters
Jeffrey Dean and Sanjay Ghemawat · 2008
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Scaled gromov hyperbolic graphs
Edmond Jonckheere, Poonsuk Lohsoonthorn, and Francis Bonahon · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Deep learning via semi-supervised embedding
Jason Weston, Frédéric Ratle, and Ronan Collobert · 2008
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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On social networks and collaborative recommendation
Ioannis Konstas, Vassilios Stathopoulos, and Joemon M. Jose · 2009
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Hyperbolic geometry of complex networks
Dmitri Krioukov, Fragkiskos Papadopoulos, Maksim Kitsak, Amin Vahdat, and Marián Boguná · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Graph kernels
S. V. N. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M Borgwardt · 2010
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Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
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Principal component analysis
Ian Jolliffe · 2011
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Low distortion delaunay embedding of trees in hyperbolic plane
Rik Sarkar · 2011
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Popularity versus similarity in growing networks
Fragkiskos Papadopoulos, Maksim Kitsak, M Ángeles Serrano, Marián Boguná, and Dmitri Krioukov · 2012
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Tree-like structure in large social and information networks
Aaron B Adcock, Blair D Sullivan, and Michael W Mahoney · 2013
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Distributed large-scale natural graph factorization
Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J Smola · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Stochastic gradient descent on riemannian manifolds
Silvere Bonnabel · 2013
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On the hyperbolicity of small-world and treelike random graphs
Wei Chen, Wenjie Fang, Guangda Hu, and Michael W Mahoney · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Spectral networks and locally connected networks on graphs international conference on learning representations (iclr2014)
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun · 2014
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg · 2014
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Network mapping by replaying hyperbolic growth
Fragkiskos Papadopoulos, Constantinos Psomas, and Dmitri Krioukov · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Deep convolutional networks on graph-structured data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Geodesic convolutional neural networks on riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
Cited alongside, same era.
Deep graph kernels
Pinar Yanardag and S.V.N. Vishwanathan · 2015
Cited alongside, same era.
Metric tree-like structures in real-world networks: an empirical study
Muad Abu-Ata and Feodor F Dragan · 2016
Cited alongside, same era.
Efficient embedding of complex networks to hyperbolic space via their laplacian
Gregorio Alanis-Lobato, Pablo Mier, and Miguel A Andrade-Navarro · 2016
Cited alongside, same era.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Cited alongside, same era.
Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein · 2016
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Graphrnn: A deep generative model for graphs
Jiaxuan You, Rex Ying, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
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Ddgk: Learning graph representations for deep divergence graph kernels
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Cited alongside, same era.
Deep neural networks for learning graph representations
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Asymmetric transitivity preserving graph embedding
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu · 2016
Cited alongside, same era.
Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
Cited alongside, same era.
Rami Al-Rfou, Dustin Zelle, and Bryan Perozzi · 2019
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Multi-relational poincaré graph embeddings
Ivana Balazevic, Carl Allen, and Timothy Hospedales · 2019
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Compositional fairness constraints for graph embeddings
Avishek Joey Bose and William Hamilton · 2019
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Hyperbolic graph convolutional neural networks
Ines Chami, Zhitao Ying, Christopher Ré, and Jure Leskovec · 2019
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Supervised community detection with line graph neural networks
Zhengdao Chen, Joan Bruna Estrach, and Lisha Li · 2019
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A modular framework for unsupervised graph representation learning
Daniel Fernando Daza Cruz, Thomas Kipf, and Max Welling · 2019
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S Du, Kangcheng Hou, Russ R Salakhutdinov, Barnabas Poczos, Ruosong Wang, and Keyulu Xu · 2019
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Deep learning for molecular design—a review of the state of the art
Daniel C Elton, Zois Boukouvalas, Mark D Fuge, and Peter W Chung · 2019
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Is a single embedding enough? learning node representations that capture multiple social contexts
Alessandro Epasto and Bryan Perozzi · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Hila Gonen and Yoav Goldberg · 2019
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Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
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Inferring concept hierarchies from text corpora via hyperbolic embeddings
Matthew Le, Stephen Roller, Laetitia Papaxanthos, Douwe Kiela, and Maximilian Nickel · 2019
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Self-attention graph pooling
Junhyun Lee, Inyeop Lee, , and Jaewoo Kang · 2019
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PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, and Alex Peysakhovich · 2019
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Hyperbolic graph neural networks
Qi Liu, Maximilian Nickel, and Douwe Kiela · 2019
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What graph neural networks cannot learn: depth vs width
Andreas Loukas · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Monet: Debiasing graph embeddings via the metadata-orthogonal training unit
John Palowitch and Bryan Perozzi · 2019
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Learning to solve np-complete problems: A graph neural network for decision tsp
Marcelo Prates, Pedro HC Avelar, Henrique Lemos, Luis C Lamb, and Moshe Y Vardi · 2019
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Netsmf: Large-scale network embedding as sparse matrix factorization
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang · 2019
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Gemsec: Graph embedding with self clustering
Benedek Rozemberczki, Ryan Davies, Rik Sarkar, and Charles Sutton · 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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Stability and generalization of graph convolutional neural networks
Saurabh Verma and Zhi-Li Zhang · 2019
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Deep graph library: Towards efficient and scalable deep learning on graphs
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, et al · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu · 2019
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Position-aware graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2019
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Numerically accurate hyperbolic embeddings using tiling-based models
Tao Yu and Christopher M De Sa · 2019
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Spectral clustering with graph neural networks for graph pooling
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 2020
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Low-dimensional hyperbolic knowledge graph embeddings
Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, and Christopher Ré · 2020
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Convolutional kernel networks for graph-structured data
Dexiong Chen, Laurent Jacob, and Julien Mairal · 2020
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
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Generalization and representational limits of graph neural networks
Vikas K Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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Grale: Designing networks for graph learning
Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, and Bryan Perozzi · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Graph embedding with personalized context distribution
Di Huang, Zihao He, Yuzhong Huang, Kexuan Sun, Sami Abu-El-Haija, Bryan Perozzi, Kristina Lerman, Fred Morstatter, and Aram Galstyan · 2020
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A survey on graph kernels
N. M. Kriege, F. D. Johansson, and C. Morris · 2020
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The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, and Vittorio Ferrari · 2020
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Graph neural networks meet neural-symbolic computing: A survey and perspective
Luis Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, and Moshe Vardi · 2020
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Graph Representation Learning via Graphical Mutual Information Maximization
Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang · 2020
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Evaluating logical generalization in graph neural networks
Koustuv Sinha, Shagun Sodhani, Joelle Pineau, and William L Hamilton · 2020
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On the equivalence between positional node embeddings and structural graph representations
Balasubramaniam Srinivasan and Bruno Ribeiro · 2020
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Just slaq when you approximate: Accurate spectral distances for web-scale graphs
Anton Tsitsulin, Marina Munkhoeva, and Bryan Perozzi · 2020
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Graph traversal with tensor functionals: A meta-algorithm for scalable learning
Elan Sopher Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, and Aram Galstyan · 2021
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Pathfinder discovery networks for neural message passing
Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, and Bryan Perozzi · 2021
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