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We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity.
A new measure of rank correlation
Maurice G Kendall · 1938
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A formal basis for the heuristic determination of minimum cost paths
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What is the distance between graphs
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On a relation between graph edit distance and maximum common subgraph
Horst Bunke · 1997
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A graph distance metric based on the maximal common subgraph
Horst Bunke and Kim Shearer · 1998
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On a connection between kernel pca and metric multidimensional scaling
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Laplacian eigenmaps for dimensionality reduction and data representation
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Substructure similarity search in graph databases
Xifeng Yan, Philip S Yu, and Jiawei Han · 2005
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Fast suboptimal algorithms for the computation of graph edit distance
Michel Neuhaus, Kaspar Riesen, and Horst Bunke · 2006
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Graph based shapes representation and recognition
Rashid Jalal Qureshi, Jean-Yves Ramel, and Hubert Cardot · 2007
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Visualizing data using t-sne
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Iam graph database repository for graph based pattern recognition and machine learning
Kaspar Riesen and Horst Bunke · 2008
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Comparison of descriptor spaces for chemical compound retrieval and classification
Nikil Wale, Ian A Watson, and George Karypis · 2008
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Approximate graph edit distance computation by means of bipartite graph matching
Kaspar Riesen and Horst Bunke · 2009
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Fast subtree kernels on graphs
Nino Shervashidze and Karsten Borgwardt · 2009
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Efficient graphlet kernels for large graph comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt · 2009
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Speeding up graph edit distance computation through fast bipartite matching
Stefan Fankhauser, Kaspar Riesen, and Horst Bunke · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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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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A novel software toolkit for graph edit distance computation
Kaspar Riesen, Sandro Emmenegger, and Horst Bunke · 2013
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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A new space for comparing graphs
Anshumali Shrivastava and Ping Li · 2014
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Approximation of graph edit distance based on hausdorff matching
Andreas Fischer, Ching Y Suen, Volkmar Frinken, Kaspar Riesen, and Horst Bunke · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Dynamic edgeconditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Community preserving network embedding
Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang · 2017
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On the exact computation of the graph edit distance
David B Blumenthal and Johann Gamper · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Dynamic network embedding: An extended approach for skip-gram based network embedding
Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang · 2018
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Neural relational inference for interacting systems
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Convolutional neural networks on graphs with fast localized spectral filtering
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Sanjay Surendranath Girija · 2016
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node2vec: Scalable feature learning for networks
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Learning convolutional neural networks for graphs
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Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Drug similarity integration through attentive multi-view graph auto-encoders
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Deep contextualized word representations
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Network embedding as matrix factorization: Unifyingdeepwalk, line, pte, and node2vec
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 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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Substructure assembling network for graph classification
Xiaohan Zhao, Bo Zong, Ziyu Guan, Kai Zhang, and Wei Zhao · 2018
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Simgnn: A neural network approach to fast graph similarity computation
Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, and Wei Wang · 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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Capsule graph neural network
Xinyi Zhang and Lihui Chen · 2019
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