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Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks.
Are powerful graph neural nets necessary? A dissection on graph classification
Chen, T., Bian, S., and Sun, Y · 1905
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Alchemy: A quantum chemistry dataset for benchmarking AI models
Chen, G., Chen, P., Hsieh, C., Lee, C., Liao, B., Liao, R., Liu, W., Qiu, J., Sun, Q., Tang, J., Zemel, R. S., and Zhang, S · 1906
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Deep graph library: Towards efficient and scalable deep learning on graphs
Wang, M., Yu, L., Zheng, D., Gan, Q., Gai, Y., Ye, Z., Li, M., Zhou, J., Huang, Q., Ma, C., Huang, Z., Guo, Q., Zhang, H., Lin, H., Zhao, J., Li, J., Smola, A. J., and Zhang, Z · 1909
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., and Hansch, C · 1991
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Chemnet: A novel neural network based method for graph/property mapping
Kireev, D. B · 1995
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Supervised neural networks for the classification of structures
Sperduti, A. and Starita, A · 1997
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The predictive toxicology challenge 2000–2001
Helma, C., King, R. D., Kramer, S., and Srinivasan, A · 2001
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Distinguishing enzyme structures from non-enzymes without alignments
Dobson, P. D. and Doig, A. J · 2003
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On graph kernels: Hardness results and efficient alternatives
Gärtner, T., Flach, P., and Wrobel, S · 2003
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Network biology: Understanding the cell’s functional organization
Barabasi, A.-L. and Oltvai, Z. N · 2004
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Brenda, the enzyme database: updates and major new developments
Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G., and Schomburg, D · 2004
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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. V. N., Smola, A. J., and Kriegel, H.-P · 2005
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Automatic generation of complementary descriptors with molecular graph networks
Merkwirth, C. and Lengauer, T · 2005
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Reality Mining: Sensing complex social systems
Eagle, N. and Pentland, A · 2006
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LIBLINEAR: A library for large linear classification
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J · 2008
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Iam graph database repository for graph based pattern recognition and machine learning
Riesen, K. and Bunke, H · 2008
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Comparison of descriptor spaces for chemical compound retrieval and classification
Wale, N., Watson, I. A., and Karypis, G · 2008
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Mining significant graph patterns by leap search
Yan, X., Cheng, H., Han, J., and Yu, P. S · 2008
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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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Efficient graphlet kernels for large graph comparison
Shervashidze, N., Vishwanathan, S. V. N., Petri, T. H., Mehlhorn, K., and Borgwardt, K. M · 2009
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On the evolution of user interaction in Facebook
Viswanath, B., Mislove, A., Cha, M., and Gummadi, K. P · 2009
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Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Easley, D. and Kleinberg, J · 2010
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LIBSVM: A library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
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What’s in a crowd? Analysis of face-to-face behavioral networks
Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.-F., and Van den Broeck, W · 2011
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Weisfeiler-Lehman graph kernels
Shervashidze, N., Schweitzer, P., van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M · 2011
Cited alongside, same era.
Scalable kernels for graphs with continuous attributes
Feragen, A., Kasenburg, N., Petersen, J., Bruijne, M. D., and M., B. K · 2013
Cited alongside, same era.
Spectral networks and deep locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2014
Cited alongside, same era.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P., O., Rupp, M., and von Lilienfeld, O. A · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
A degeneracy framework for graph similarity
Nikolentzos, G., Meladianos, P., Limnios, S., and Vazirgiannis, M · 2018
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Grakel: A graph kernel library in python
Siglidis, G., Nikolentzos, G., Limnios, S., Giatsidis, C., Skianis, K., and Vazirgiannis, M · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, 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., and Jegelka, S · 2018
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Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M · 2018
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Cited alongside, same era.
Halting in random walk kernels
Sugiyama, M. and Borgwardt, K. M · 2015
Cited alongside, same era.
Deep graph kernels
Yanardag, P. and Vishwanathan, S. V. N · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., X., B., and Vandergheynst, P · 2016
Cited alongside, same era.
The multiscale Laplacian graph kernel
Kondor, R. and Pan, H · 2016
Cited alongside, same era.
On valid optimal assignment kernels and applications to graph classification
Kriege, N. M., Giscard, P.-L., and Wilson, R. C · 2016
Cited alongside, same era.
Faster kernel for graphs with continuous attributes via hashing
Morris, C., Kriege, N. M., Kersting, K., and Mutzel, P · 2016
Cited alongside, same era.
Cormorant: Covariant molecular neural networks
Anderson, B. M., Hy, T., and Kondor, R · 2019
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A fair comparison of graph neural networks for graph classification
Errica, F., Podda, M., Bacciu, D., and Micheli, A · 2019
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IPC: A benchmark data set for learning with graph-structured data
Ferber, P., Ma, T., Huo, S., Chen, J., and Katz, M · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Distribution of node embeddings as multiresolution features for graphs
Heimann, M., Safavi, T., and Koutra, D · 2019
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Understanding attention and generalization in graph neural networks
Knyazev, B., Taylor, G. W., and Amer, M. R · 2019
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A unifying view of explicit and implicit feature maps of graph kernels
Kriege, N. M., Neumann, M., Morris, C., Kersting, K., and Mutzel, P · 2019
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A persistent Weisfeiler-Lehman procedure for graph classification
Rieck, B., Bock, C., and Borgwardt, K. M · 2019
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Wasserstein Weisfeiler-Lehman graph kernels
Togninalli, M., Ghisu, E., Llinares-López, F., Rieck, B., and Borgwardt, K. M · 2019
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S · 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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Machine learning on graphs: A model and comprehensive taxonomy
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., and Murphy, K · 2020
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., and Bresson, X · 2020
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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
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Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 2020
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A survey on graph kernels
Kriege, N. M., Johansson, F. D., and Morris, C · 2020
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Temporal graph kernels for classifying dissemination processes
Oettershagen, L., Kriege, N. M., Morris, C., and Mutzel, P · 2020
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An API oriented open-source python framework for unsupervised learning on graphs
Rozemberczki, B., Kiss, O., and Sarkar, R · 2020
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A deep learning approach to antibiotic discovery
Stokes, J., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N., MacNair, C., French, S., Carfrae, L., Bloom-Ackerman, Z., Tran, V., Chiappino-Pepe, A., Badran, A., Andrews, I., Chory, E., Church, G., Brown, E., Jaakkola, T., Barzilay, R., and Collins, J · 2020
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