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Graph representation learning has attracted increasing research attention.
An algorithm for subgraph isomorphism
Julian R Ullmann. 1976 · 1976
Earlier work this paper cites.
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 · 1991
Earlier work this paper cites.
Q-learning
Christopher JCH Watkins and Peter Dayan. 1992 · 1992
Earlier work this paper cites.
Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig. 2003 · 2003
Earlier work this paper cites.
Graph-based anomaly detection. In Proceedings of the ACM SIGKDD . 631–636
Caleb C Noble and Diane J Cook. 2003 · 2003
Earlier work this paper cites.
Statistical evaluation of the Predictive Toxicology Challenge
Hannu Toivonen, Ashwin Srinivasan, and Christoph Helma. 2003 · 2003
Earlier work this paper cites.
Efficient graph-based image segmentation
Pedro F Felzenszwalb and Daniel P Huttenlocher. 2004 · 2004
Earlier work this paper cites.
Shortest-path kernels on graphs. In Proceedings of the IEEE ICDM . 8–pp
Karsten M Borgwardt and Hans-Peter Kriegel. 2005 · 2005
Earlier work this paper cites.
Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel. 2005 · 2005
Earlier work this paper cites.
Comparison of descriptor spaces for chemical compound retrieval and classification
Nikil Wale, Ian A Watson, and George Karypis. 2008 · 2008
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
Nino Shervashidze, S. V. N. Vishwanathan, Tobias H. Petri, Kurt Mehlhorn, and Karsten M. Borgwardt. 2009 · 2009
Earlier work this paper cites.
Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. 2011 · 2011
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs. In Proceedings of the ICLR
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013 · 2013
Earlier work this paper cites.
Graph based anomaly detection and description: a survey
Leman Akoglu, Hanghang Tong, and Danai Koutra. 2015 · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints. In Proceedings of the NeurIPS . 2224–2232
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams. 2015 · 2015
Earlier work this paper cites.
Deep graph kernels. In Proceedings of the ACM SIGKDD
Pinar Yanardag and SVN Vishwanathan. 2015 · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the NeurIPS . 3844–3852
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the ICLR
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Gated Graph Sequence Neural Networks. In Proceedings of the ACM ICML
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2016 · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization. In Proceedings of the NeurIPS . 271–279
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016 · 2016
Cited alongside, same era.
Inductive representation learning on large graphs. In Proceedings of the NeurIPS . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Dynamic edge-conditioned filters in convolutional neural networks on graphs. In Proceedings of the IEEE CVPR
Martin Simonovsky and Nikos Komodakis. 2017 · 2017
Cited alongside, same era.
Graph Attention Networks. In Proceedings of the ICLR
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective. In Proceedings of the WWW
Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang. 2019 · 2019
Later among the works it cites.
Graph convolutional networks with eigenpooling. In Proceedings of the ACM SIGKDD . 723–731
Yao Ma, Suhang Wang, Charu C Aggarwal, and Jiliang Tang. 2019 · 2019
Later among the works it cites.
Deep Graph Infomax.. In Proceedings of the ICLR
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019 · 2019
Later among the works it cites.
Capsule Graph Neural Network. In Proceedings of the ICLR
Zhang Xinyi and Lihui Chen. 2019 · 2019
Later among the works it cites.
How Powerful are Graph Neural Networks?. In Proceedings of the ICLR
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Later among the works it cites.
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A comprehensive survey of graph embedding: Problems, techniques, and applications
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018 · 2018
Cited alongside, same era.
Towards sparse hierarchical graph classifiers. In NeurIPS 2018 Workshop on Relational Representation Learning
Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the NAACL . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Anonymous Walk Embeddings. In Proceedings of the ACM ICML . 2191–2200
Sergey Ivanov and Evgeny Burnaev. 2018 · 2018
Cited alongside, same era.
Drug similarity integration through attentive multi-view graph auto-encoders
Tengfei Ma, Cao Xiao, Jiayu Zhou, and Fei Wang. 2018 · 2018
Cited alongside, same era.
Subgraph Pattern Neural Networks for High-Order Graph Evolution Prediction.. In Proceedings of the AAAI . 3778–3787
Changping Meng, S Chandra Mouli, Bruno Ribeiro, and Jennifer Neville. 2018 · 2018
Cited alongside, same era.
Motifnet: a motif-based graph convolutional network for directed graphs. In Proceedings of the IEEE DSW . IEEE, 225–228
Federico Monti, Karl Otness, and Michael M Bronstein. 2018 · 2018
Cited alongside, same era.
Qi Xuan, Jinhuan Wang, Minghao Zhao, Junkun Yuan, Chenbo Fu, Zhongyuan Ruan, and Guanrong Chen. 2019 · 2019
Later among the works it cites.
Xlnet: Generalized autoregressive pretraining for language understanding. In Proceedings of the NeurIPS . 5753–5763
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Later among the works it cites.
Oag: Toward linking large-scale heterogeneous entity graphs. In Proceedings of the ACM SIGKDD . 2585–2595
Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, et al · 2019
Later among the works it cites.
Subgraph Neural Networks. In Proceedings of the NeurIPS
Emily Alsentzer, Samuel G Finlayson, Michelle M Li, and Marinka Zitnik. 2020 · 2020
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Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M Bronstein. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE CVPR . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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A survey on graph kernels
Nils M Kriege, Fredrik D Johansson, and Christopher Morris. 2020 · 2020
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GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. In Proceedings of the ACM SIGKDD . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Later among the works it cites.
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations.. In Proceedings of the AAAI . 5470–5477
Ekagra Ranjan, Soumya Sanyal, and Partha P Talukdar. 2020 · 2020
Later among the works it cites.
Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks
Qingyun Sun, Hao Peng, Jianxin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S Yu, and Lifang He. 2020b · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020 · 2020
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Learning convolutional neural networks for graphs. In Proceedings of the ACM ICML . 2014–2023
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016 · 2023
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