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Link prediction is one important application of graph neural networks (GNNs).
An optimal lower bound on the number of variables for graph identification
Cai, J.-Y., Fürer, M., and Immerman, N · 1992
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Emergence of scaling in random networks
Barabási, A.-L. and Albert, R · 1999
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Friends and neighbors on the web
Adamic, L. A. and Adar, E · 2003
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Link prediction using supervised learning
Al Hasan, M., Chaoji, V., Salem, S., and Zaki, M · 2006
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The link-prediction problem for social networks
Liben-Nowell, D. and Kleinberg, J · 2007
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Probabilistic matrix factorization
Mnih, A. and Salakhutdinov, R. R · 2008
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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Predicting missing links via local information
Zhou, T., Lü, L., and Zhang, Y.-C · 2009
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Link prediction via matrix factorization
Menon, A. K. and Elkan, C · 2011
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Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., Van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M · 2011
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A review of relational machine learning for knowledge graphs
Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E · 2015
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node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Learning convolutional neural networks for graphs
Niepert, M., Ahmed, M., and Kutzkov, K · 2016
Cited alongside, same era.
Complex embeddings for simple link prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., and Bouchard, G · 2016
Cited alongside, same era.
Descriptive complexity, canonisation, and definable graph structure theory , volume 47
Grohe, M · 2017
Cited alongside, same era.
Boostgapfill: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods
Oyetunde, T., Zhang, M., Chen, Y., Tang, Y., and Lo, C · 2017
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. v. d., Titov, I., and Welling, M · 2017
Cited alongside, same era.
Distance encoding: Design provably more powerful neural networks for graph representation learning
Li, P., Wang, Y., Wang, H., and Leskovec, J · 2020
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Inductive relation prediction by subgraph reasoning
Teru, K., Denis, E., and Hamilton, W · 2020
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Inductive matrix completion based on graph neural networks
Zhang, M. and Chen, Y · 2020
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Line graph neural networks for link prediction
Cai, L., Li, J., Wang, J., and Ji, S · 2021
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Indigo: Gnn-based inductive knowledge graph completion using pair-wise encoding
Liu, S., Grau, B., Horrocks, I., and Kostylev, E · 2021
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Davidson, T. R., Falorsi, L., Cao, N. D., Kipf, T., and Tomczak, J. M · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
Cited alongside, same era.
Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
Cited alongside, same era.
Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M · 2019
Cited alongside, same era.
Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
Cited alongside, same era.
Relation structure-aware heterogeneous graph neural network
Zhu, S., Zhou, C., Pan, S., Zhu, X., and Wang, B · 2019
Cited alongside, same era.
Morris, C., Lipman, Y., Maron, H., Rieck, B., Kriege, N. M., Grohe, M., Fey, M., and Borgwardt, K · 2021
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Neural link prediction with walk pooling
Pan, L., Shi, C., and Dokmanić, I · 2021
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Link prediction with persistent homology: An interactive view
Yan, Z., Ma, T., Gao, L., Tang, Z., and Chen, C · 2021
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Identity-aware graph neural networks
You, J., Gomes-Selman, J., Ying, R., and Leskovec, J · 2021
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Labeling trick: A theory of using graph neural networks for multi-node representation learning
Zhang, M., Li, P., Xia, Y., Wang, K., and Jin, L · 2021
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Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhu, Z., Zhang, Z., Xhonneux, L.-P., and Tang, J · 2021
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