Bibliography

[1] Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., Shusterman, A.J., and Hansch, C. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. J. Med. Chem. 34(2):786-797 (1991).

[2] Helma, C., King, R. D., Kramer, S., and Srinivasan, A. The Predictive Toxicology Challenge 2000–2001. Bioinformatics, 2001, 17, 107-108. The Predictive Toxicology Challenge

[3] Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., Borgwardt, K.M.: Scalable kernels for graphs with continuous attributes. In: C.J.C. Burges, L. Bottou, Z. Ghahramani, K.Q. Weinberger (eds.) NIPS, pp. 216-224 (2013).

[4] K. M. Borgwardt, C. S. Ong, S. Schoenauer, S. V. N. Vishwanathan, A. J. Smola, and H. P. Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(Suppl 1):i47–i56, Jun 2005.

[5] I. Schomburg, A. Chang, C. Ebeling, M. Gremse, C. Heldt, G. Huhn, and D. Schomburg. Brenda, the enzyme database: updates and major new developments. Nucleic Acids Research, 32D:431–433, 2004.

[6] P. D. Dobson and A. J. Doig. Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol., 330(4):771–783, Jul 2003.

[7] Sutherland, J. J.; O’Brien, L. A. & Weaver, D. F. Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships. J. Chem. Inf. Comput. Sci., 2003, 43, 1906-1915.

[8] N. Wale and G. Karypis. Comparison of descriptor spaces for chemical compound retrieval and classification. In Proc. of ICDM, pages 678–689, Hong Kong, 2006.

[9] Pubchem

[10] http://image.diku.dk/aasa/papers/graphkernels_nips_erratum.pdf

[11] M. Neumann, P. Moreno, L. Antanas, R. Garnett, K. Kersting. Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping. Eleventh Workshop on Mining and Learning with Graphs (MLG-13), Chicago, Illinois, USA, 2013.

[12] hhttp://www.first-mm.eu/data.html

[13] M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting. Propagation kernels: efficient graph kernels from propagated information. Machine Learning, 102(2):209–245, 2016

[14] Pinar Yanardag and S.V.N. Vishwanathan. 2015. Deep Graph Kernels. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, 1365-1374.

[15] Francesco Orsini, Paolo Frasconi, and Luc De Raedt. 2015 Graph invariant kernels. In Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI’15), Qiang Yang and Michael Wooldridge (Eds.). AAAI Press 3756-3762.

[16] Riesen, K. and Bunke, H.: IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning. In: da Vitora Lobo, N. et al. (Eds.), SSPR&SPR 2008, LNCS, vol. 5342, pp. 287-297, 2008.

[17] AIDS Antiviral Screen Data (2004)

[18] S. A. Nene, S. K. Nayar and H. Murase. Columbia Object Image Library, Technical Report, Department of Computer Science, Columbia University CUCS-006-96, Feb. 1996.

[19] NIST Special Database 4

[20] Jeroen Kazius, Ross McGuire and, and Roberta Bursi. Derivation and Validation of Toxicophores for Mutagenicity Prediction, Journal of Medicinal Chemistry 2005 48 (1), 312-320

[21] Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel. Faster Kernels for Graphs with Continuous Attributes via Hashing, IEEE International Conference on Data Mining (ICDM) 2016

[22] Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. 2011. Weisfeiler-Lehman Graph Kernels. J. Mach. Learn. Res. 12 (November 2011), 2539-2561.

[23] Nils Kriege, Petra Mutzel. 2012. Subgraph Matching Kernels for Attributed Graphs. International Conference on Machine Learning 2012.

[24] Tox21 Data Challenge 2014

[25] Nils M. Kriege, Matthias Fey, Denis Fisseler, Petra Mutzel, Frank Weichert. Recognizing Cuneiform Signs Using Graph Based Methods. International Workshop on Cost-Sensitive Learning (COST), SIAM International Conference on Data Mining (SDM) 2018, 31-44.

[26] A Repository of Benchmark Graph Datasets for Graph Classification

[27] Boris Knyazev, Graham W. Taylor, Mohamed R. Amer. Understanding Attention and Generalization in Graph Neural Networks. Neural Information Processing Systems (NeurIPS) 2019, 4204-4214.

[28] Xifeng Yan, Hong Cheng, Jiawei Han, Philip S. Yu. Mining Significant Graph Patterns by Leap Search. ACM SIGMOD International Conference on Management of Data 2008, 433–444, Chemical Datasets.

[29] Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

[30] An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

[31] Xavier Bresson, Thomas Laurent. A Two-Step Graph Convolutional Decoder for Molecule Generation. Workshop on Machine Learning and the Physical Sciences. 2019.

[32] Lutz Oettershagen, Nils Kriege, Christopher Morris, Petra Mutzel. Temporal Graph Kernels for Classifying Dissemination Processes. SIAM International Conference on Data Mining (SDM) 2020.

[33] PyTorch Geometric datasets

[34] Zhenqin Wu, Bharath Ramsundar, Evan Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh Pappu, Karl Leswing, Vijay Pande. MoleculeNet: A Benchmark for Molecular Machine Learning. Chemical Science. 9. 2017.

[35] Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp, O. Anatole von Lilienfeld. Quantum Chemistry Structures and Properties of 134 kilo Molecules, Scientific Data 1: 140022, 2014.

[36] Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, Klaus-Robert Müller. Machine Learning of Accurate Energy-Conserving Molecular Force Fields. Science Advances 3(5). 2017.