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Graph Neural Networks (GNNs) have proven to be useful for many different practical applications.
Multi-scale attributed node embedding
Rozemberczki, B.; Allen, C.; and Sarkar, R. 2019 · 1909
Earlier work this paper cites.
Concerning nonnegative matrices and doubly stochastic matrices
Sinkhorn, R.; and Knopp, P. 1967 · 1967
Earlier work this paper cites.
Emergence of scaling in random networks
Barabasi, A. L.; and Albert, R. 1999 · 1999
Earlier work this paper cites.
Modeling polypharmacy side effects with graph convolutional networks
Zitnik, M.; Agrawal, M.; and Leskovec, J. 2018 · 1999
Earlier work this paper cites.
Iterative classification in relational data
J. Neville, D. J. 2000 · 2000
Earlier work this paper cites.
Birds of a feather: Homophily in social networks
McPherson, M.; Smith-Lovin, L.; and Cook, J. M. 2001 · 2001
Earlier work this paper cites.
Link-Based Classification
Lu, Q.; and Getoor, L. 2003 · 2003
Earlier work this paper cites.
Understanding belief propagation and its generalizations
Yedidia, J. S.; Freeman, W. T.; and Weiss, Y. 2003 · 2003
Earlier work this paper cites.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Hu, W.; Fey, M.; Zitnik, M.; Dong, Y.; Ren, H.; Liu, B.; Catasta, M.; and Leskovec, J. 2020 · 2005
Earlier work this paper cites.
Deepergcn: All you need to train deeper gcns
Li, G.; Xiong, C.; Thabet, A.; and Ghanem, B. 2020 · 2006
Earlier work this paper cites.
Cautious inference in collective classification
McDowell, L. K.; Gupta, K. M.; and Aha, D. W. 2007 · 2007
Earlier work this paper cites.
Netprobe: a fast and scalable system for fraud detection in online auction networks
Pandit, S.; Chau, D. H.; Wang, S.; and Faloutsos, C. 2007 · 2007
Earlier work this paper cites.
The graph neural network model
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2008 · 2008
Earlier work this paper cites.
Collective classification in network data
Sen, P.; Namata, G.; Bilgic, M.; Getoor, L.; Galligher, B.; and Eliassi-Rad, T. 2008 · 2008
Earlier work this paper cites.
Unifying guilt-by-association approaches: Theorems and fast algorithms
Koutra, D.; Ke, T.-Y.; Kang, U.; Chau, D. H. P.; Pao, H.-K. K.; and Faloutsos, C. 2011 · 2011
Earlier work this paper cites.
Query-driven active surveying for collective classification
Namata, G.; London, B.; Getoor, L.; and Huang, B. 2012 · 2012
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Transforming Graph Data for Statistical Relational Learning
Rossi, R. A.; McDowell, L. K.; Aha, D. W.; and Neville, J. 2012 · 2012
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Collective Classification of Network Data
London, B.; and Getoor, L. 2014 · 2014
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Linearized and single-pass belief propagation
Gatterbauer, W.; Günnemann, S.; Koutra, D.; and Faloutsos, C. 2015 · 2015
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The network data repository with interactive graph analytics and visualization
Rossi, R. A.; and Ahmed, N. K. 2015 · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
Representation Learning on Graphs with Jumping Knowledge Networks
Xu, K.; Li, C.; Tian, Y.; Sonobe, T.; Kawarabayashi, K.; and Jegelka, S. 2018 · 2018
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Graph convolutional neural networks for web-scale recommender systems
Ying, R.; He, R.; Chen, K.; Eksombatchai, P.; Hamilton, W. L.; and Leskovec, J. 2018 · 2018
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MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing
Abu-El-Haija, S.; Perozzi, B.; Kapoor, A.; Harutyunyan, H.; Alipourfard, N.; Lerman, K.; Steeg, G. V.; and Galstyan, A. 2019 · 2019
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DeepGCNs: Can GCNs Go as Deep as CNNs?
Li, G.; Müller, M.; Thabet, A.; and Ghanem, B. 2019 · 2019
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GMNN: Graph Markov Neural Networks
Qu, M.; Bengio, Y.; and Tang, J. 2019 · 2019
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Graph Agreement Models for Semi-Supervised Learning
Stretcu, O.; Viswanathan, K.; Movshovitz-Attias, D.; Platanios, E.; Ravi, S.; and Tomkins, A. 2019 · 2019
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Revisiting semi-supervised learning with graph embeddings
Yang, Z.; Cohen, W.; and Salakhudinov, R. 2016 · 2016
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Protein interface prediction using graph convolutional networks
Fout, A.; Byrd, J.; Shariat, B.; and Ben-Hur, A. 2017 · 2017
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Inductive Representation Learning on Large Graphs
Hamilton, W. L.; Ying, R.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Visibility of minorities in social networks
Karimi, F.; Génois, M.; Wagner, C.; Singer, P.; and Strohmaier, M. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Cited alongside, same era.
Learning Role-based Graph Embeddings
Ahmed, N. K.; Rossi, R.; Lee, J. B.; Willke, T. L.; Zhou, R.; Kong, X.; and Eldardiry, H. 2018 · 2018
Cited alongside, same era.
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Simplifying Graph Convolutional Networks
Wu, F.; Souza, A.; Zhang, T.; Fifty, C.; Yu, T.; and Weinberger, K. 2019 · 2019
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Groupinn: Grouping-based interpretable neural network for classification of limited, noisy brain data
Yan, Y.; Zhu, J.; Duda, M.; Solarz, E.; Sripada, C.; and Koutra, D. 2019 · 2019
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Simple and Deep Graph Convolutional Networks
Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; and Li, Y. 2020 · 2020
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Enhancing graph neural network-based fraud detectors against camouflaged fraudsters
Dou, Y.; Liu, Z.; Sun, L.; Deng, Y.; Peng, H.; and Yu, P. S. 2020 · 2020
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Geom-GCN: Geometric Graph Convolutional Networks
Pei, H.; Wei, B.; Chang, K. C.-C.; Lei, Y.; and Yang, B. 2020 · 2020
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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Rong, Y.; Huang, W.; Xu, T.; and Huang, J. 2020 · 2020
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On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
Rossi, R. A.; Jin, D.; Kim, S.; Ahmed, N. K.; Koutra, D.; and Lee, J. B. 2020 · 2020
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Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Zhu, J.; Yan, Y.; Zhao, L.; Heimann, M.; Akoglu, L.; and Koutra, D. 2020 · 2020
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Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Yan, Y.; Hashemi, M.; Swersky, K.; Yang, Y.; and Koutra, D. 2021 · 2021
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