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Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.
On lipschitz embedding of finite metric spaces in hilbert space
Bourgain, J · 1985
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The geometry of graphs and some of its algorithmic applications
Linial, N., London, E., and Rabinovich, Y · 1995
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Networks, dynamics, and the small-world phenomenon
Watts, D. J · 1999
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Belkin, M. and Niyogi, P · 2002
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On graph kernels: Hardness results and efficient alternatives
Gärtner, T., Flach, P., and Wrobel, S · 2003
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Scaling personalized web search
Jeh, G. and Widom, J · 2003
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Marginalized kernels between labeled graphs
Kashima, H., Tsuda, K., and Inokuchi, A · 2003
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Shortest-path kernels on graphs
Borgwardt, K. M. and Kriegel, H.-P · 2005
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Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S., Smola, A. J., and Kriegel, H.-P · 2005
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Graph evolution: Densification and shrinking diameters
Leskovec, J., Kleinberg, J., and Faloutsos, C · 2007
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
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Halting in random walk kernels
Sugiyama, M. and Borgwardt, K. M · 2015
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Deep graph kernels
Yanardag, P. and Vishwanathan, S. V. N · 2015
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node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Predicting multicellular function through multi-layer tissue networks
Zitnik, M. and Leskovec, J · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
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Kipf, T. N. and Welling, M · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J
Cited in the paper.
Representation learning on graphs: Methods and applications
Hamilton, W. L., Ying, R., and Leskovec, J
Cited in the paper.
Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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Learning a sat solver from single-bit supervision
Selsam, D., Lamm, M., Bunz, B., Liang, P., de Moura, L., and Dill, D. L · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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