{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:41:53Z","timestamp":1782834113159,"version":"3.54.5"},"reference-count":133,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T00:00:00Z","timestamp":1578960000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T00:00:00Z","timestamp":1578960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Netw Sci"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner\u2019s guide to kernel-based graph classification.<\/jats:p>","DOI":"10.1007\/s41109-019-0195-3","type":"journal-article","created":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T16:14:32Z","timestamp":1579018472000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":303,"title":["A survey on graph kernels"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-2645-947X","authenticated-orcid":false,"given":"Nils M.","family":"Kriege","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredrik D.","family":"Johansson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Morris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,14]]},"reference":[{"issue":"10","key":"195_CR1","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/0020-0271(73)90059-4","volume":"9","author":"GW Adamson","year":"1973","unstructured":"Adamson, GW, Bush JA (1973) A method for the automatic classification of chemical structures. Inf Storage Retrieval 9(10):561\u2013568. doi:10.1016\/0020-0271(73)90059-4.","journal-title":"Inf Storage Retrieval"},{"key":"195_CR2","doi-asserted-by":"publisher","unstructured":"Ahmed, NK, Willke T, Rossi RA (2016) Estimation of local subgraph counts In: IEEE International Conference on Big Data, 1\u201310. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/bigdata.2016.7840651.","DOI":"10.1109\/bigdata.2016.7840651"},{"key":"195_CR3","doi-asserted-by":"publisher","unstructured":"Aiolli, F, Donini M, Navarin N, Sperduti A (2015) Multiple graph-kernel learning In: IEEE Symposium Series on Computational Intelligence, 1607\u20131614. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/ssci.2015.226.","DOI":"10.1109\/ssci.2015.226"},{"key":"195_CR4","doi-asserted-by":"publisher","unstructured":"Alon, N, Spencer JH (2004) The probabilistic method. Wiley. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/0471722154.ch1.","DOI":"10.1002\/0471722154.ch1"},{"key":"195_CR5","doi-asserted-by":"publisher","unstructured":"Babai, L, Kucera L (1979) Canonical labelling of graphs in linear average time In: Annual Symposium on Foundations of Computer Science, 39\u201346. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/sfcs.1979.8.","DOI":"10.1109\/sfcs.1979.8"},{"key":"195_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-662-44415-3_1","volume-title":"Lecture Notes in Computer Science","author":"Lu Bai","year":"2014","unstructured":"Bai, L, Ren P, Bai X, Hancock ER (2014) A graph kernel from the depth-based representation In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition, 1\u201311. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-662-44415-3_1."},{"key":"195_CR7","doi-asserted-by":"publisher","unstructured":"Bai, L, Rossi L, Zhang Z, Hancock ER (2015) An aligned subtree kernel for weighted graphs In: International Conference on Machine Learning, 30\u201339. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/icpr.2016.7899666.","DOI":"10.1109\/icpr.2016.7899666"},{"issue":"1-2","key":"195_CR8","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s10994-008-5059-5","volume":"72","author":"MF Balcan","year":"2008","unstructured":"Balcan, MF, Blum A, Srebro N (2008) A theory of learning with similarity functions. Mach Learn 72(1-2):89\u2013112.","journal-title":"Mach Learn"},{"key":"195_CR9","unstructured":"Borgwardt, KM (2007) Graph kernels. Phd thesis, Ludwig Maximilians University Munich."},{"key":"195_CR10","doi-asserted-by":"publisher","unstructured":"Borgwardt, KM, Kriegel HP (2005) Shortest-path kernels on graphs In: IEEE International Conference on Data Mining, 74\u201381. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/icdm.2005.132.","DOI":"10.1109\/icdm.2005.132"},{"issue":"Supplement 1","key":"195_CR11","doi-asserted-by":"publisher","first-page":"i47","DOI":"10.1093\/bioinformatics\/bti1007","volume":"21","author":"KM Borgwardt","year":"2005","unstructured":"Borgwardt, KM, Ong CS, Sch\u00f6nauer S, Vishwanathan SVN, Smola AJ, Kriegel HP (2005) Protein function prediction via graph kernels. Bioinformatics 21(Supplement 1):i47\u2013i56.","journal-title":"Bioinformatics"},{"key":"195_CR12","doi-asserted-by":"publisher","unstructured":"Borgwardt, KM, Kriegel HP, Vishwanathan S, Schraudolphs NN (2007) Graph kernels for disease outcome prediction from protein-protein interaction networks In: Biocomputing 2007, World Scientific, 4\u201315. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1142\/9789812772435_0002.","DOI":"10.1142\/9789812772435_0002"},{"key":"195_CR13","doi-asserted-by":"publisher","unstructured":"Bressan, M, Chierichetti F, Kumar R, Leucci S, Panconesi A (2017) Counting graphlets: Space vs time In: ACM International Conference on Web Search and Data Mining, 557\u2013566. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3018661.3018732.","DOI":"10.1145\/3018661.3018732"},{"issue":"2","key":"195_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1459352.1459353","volume":"41","author":"Nathan Brown","year":"2009","unstructured":"Brown, N (2009) Chemoinformatics \u2013 an introduction for computer scientists. ACM Comput Surv 41(2). https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/1459352.1459353.","journal-title":"ACM Computing Surveys"},{"issue":"16","key":"195_CR15","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1093\/bioinformatics\/btm298","volume":"23","author":"A Ceroni","year":"2007","unstructured":"Ceroni, A, Costa F, Frasconi P (2007) Classification of small molecules by two- and three-dimensional decomposition kernels. Bioinformatics 23(16):2038\u20132045. doi:10.1093\/bioinformatics\/btm298.","journal-title":"Bioinformatics"},{"issue":"3","key":"195_CR16","doi-asserted-by":"publisher","first-page":"27:1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1\u201327:27.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"195_CR17","doi-asserted-by":"publisher","unstructured":"Chen, X, Lui JCS (2016) Mining graphlet counts in online social networks In: IEEE International Conference on Data Mining, 71\u201380. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/icdm.2016.0018.","DOI":"10.1109\/icdm.2016.0018"},{"issue":"3","key":"195_CR18","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297.","journal-title":"Mach Learn"},{"key":"195_CR19","first-page":"255","volume-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10)","author":"F Costa","year":"2010","unstructured":"Costa, F, De Grave K (2010) Fast Neighborhood Subgraph Pairwise Distance Kernel. In: F\u00fcrnkranz J Joachims T (eds)Proceedings of the 27th International Conference on Machine Learning (ICML-10), 255\u2013262.. Omnipress, Haifa. https:\/\/2.zoppoz.workers.dev:443\/http\/www.icml2010.org\/papers\/347.pdf."},{"key":"195_CR20","doi-asserted-by":"publisher","unstructured":"Da San Martino, G, Navarin N, Sperduti A (2012a) A memory efficient graph kernel In: International Joint Conference on Neural Networks, 1\u20137. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/ijcnn.2012.6252831.","DOI":"10.1109\/ijcnn.2012.6252831"},{"key":"195_CR21","doi-asserted-by":"publisher","unstructured":"Da San Martino, G, Navarin N, Sperduti A (2012b) A tree-based kernel for graphs In: SIAM Conference of Data Mining, 975\u2013986. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1137\/1.9781611972825.84.","DOI":"10.1137\/1.9781611972825.84"},{"key":"195_CR22","unstructured":"Daylight, CIS (2008) Daylight theory manual v4.9. https:\/\/2.zoppoz.workers.dev:443\/http\/www.daylight.com\/dayhtml\/doc\/theory."},{"issue":"2","key":"195_CR23","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1021\/jm00106a046","volume":"34","author":"Asim Kumar Debnath","year":"1991","unstructured":"Debnath, AK, Lopez de Compadre RL, Debnath G, Shusterman AJ, Hansch C (1991) Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. J Med Chem 34(2):786\u2013797.","journal-title":"Journal of Medicinal Chemistry"},{"key":"195_CR24","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/978-3-642-40988-2_39","volume-title":"Advanced Information Systems Engineering","author":"Gerben K. D. de Vries","year":"2013","unstructured":"de Vries, GKD (2013) A fast approximation of the Weisfeiler-Lehman graph kernel for rdf data In: European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, 606\u2013621. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-642-40988-2_39."},{"issue":"4","key":"195_CR25","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1016\/S0022-2836(03)00628-4","volume":"330","author":"PD Dobson","year":"2003","unstructured":"Dobson, PD, Doig AJ (2003) Distinguishing enzyme structures from non-enzymes without alignments. J Mol Biol 330(4):771\u2013783.","journal-title":"J Mol Biol"},{"issue":"5","key":"195_CR26","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant, JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of mdl keys for use in drug discovery. J Chem Inf Comput Sci 42(5):1273\u20131280.","journal-title":"J Chem Inf Comput Sci"},{"key":"195_CR27","unstructured":"Duvenaud, DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints In: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 2224\u20132232."},{"issue":"3\u20134","key":"195_CR28","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C, Roth A, et al. (2014) The algorithmic foundations of differential privacy. Found Trends\u00ae Theor Comput Sci 9(3\u20134):211\u2013407.","journal-title":"Found Trends\u00ae Theor Comput Sci"},{"key":"195_CR29","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: A library for large linear classification. J Mach Learn Res 9:1871\u20131874.","journal-title":"J Mach Learn Res"},{"key":"195_CR30","unstructured":"Feragen, A, Kasenburg N, Petersen J, Bruijne MD, M BK (2013) Scalable kernels for graphs with continuous attributes In: Advances in Neural Information Processing Systems, 216\u2013224. erratum available at https:\/\/2.zoppoz.workers.dev:443\/http\/image.diku.dk\/aasa\/papers\/graphkernels_nips_erratum.pdf."},{"key":"195_CR31","doi-asserted-by":"publisher","unstructured":"Fey, M, Lenssen JE, Weichert F, M\u00fcller H (2018) SplineCNN: Fast geometric deep learning with continuous b-spline kernels In: IEEE Conference on Computer Vision and Pattern Recognition, 869\u2013877. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/cvpr.2018.00097.","DOI":"10.1109\/cvpr.2018.00097"},{"issue":"5","key":"195_CR32","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"Fleiss, JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378.","journal-title":"Psychol Bull"},{"key":"195_CR33","doi-asserted-by":"publisher","unstructured":"Fr\u00f6hlich, H, Wegner JK, Sieker F, Zell A (2005) Optimal assignment kernels for attributed molecular graphs In: International Conference on Machine learning, 225\u2013232. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/1102351.1102380.","DOI":"10.1145\/1102351.1102380"},{"key":"195_CR34","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-540-45167-9_11","volume-title":"Learning Theory and Kernel Machines","author":"Thomas G\u00e4rtner","year":"2003","unstructured":"G\u00e4rtner, T, Flach P, Wrobel S (2003) On graph kernels: Hardness results and efficient alternatives In: Learning Theory and Kernel Machines, 129\u2013143.. Springer. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-540-45167-9_11."},{"key":"195_CR35","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.cosrev.2017.11.002","volume":"27","author":"S Ghosh","year":"2018","unstructured":"Ghosh, S, Das N, Gon\u00e7alves T, Quaresma P, Kundu M (2018) The journey of graph kernels through two decades. Comput Sci Rev 27:88\u2013111.","journal-title":"Comput Sci Rev"},{"key":"195_CR36","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"J Gilmer","year":"2017","unstructured":"Gilmer, J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural Message Passing for Quantum Chemistry. In: Precup D Whye Teh Y (eds)Proceedings of the 34th International Conference on Machine Learning.. PMLR, Sydney. https:\/\/2.zoppoz.workers.dev:443\/http\/proceedings.mlr.press\/v70\/gilmer17a.html."},{"key":"195_CR37","doi-asserted-by":"publisher","unstructured":"Grauman, K, Darrell T (2007a) Approximate correspondences in high dimensions In: Advances in Neural Information Processing Systems, 505\u2013512. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.7551\/mitpress\/7503.003.0068.","DOI":"10.7551\/mitpress\/7503.003.0068"},{"key":"195_CR38","unstructured":"Grauman, K, Darrell T (2007b) The pyramid match kernel: Efficient learning with sets of features. J Mach Learn Res 8(Apr):725\u2013760."},{"key":"195_CR39","first-page":"1025","volume":"abs\/1706.02216","author":"WL Hamilton","year":"2017","unstructured":"Hamilton, WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. CoRR abs\/1706.02216:1025\u20131035. https:\/\/2.zoppoz.workers.dev:443\/http\/arxiv.org\/abs\/1706.02216.","journal-title":"CoRR"},{"key":"195_CR40","doi-asserted-by":"publisher","unstructured":"Harchaoui, Z, Bach F (2007) Image classification with segmentation graph kernels In: IEEE Conference on Computer Vision and Pattern Recognition, 1\u20138. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/cvpr.2007.383049.","DOI":"10.1109\/cvpr.2007.383049"},{"key":"195_CR41","unstructured":"Haussler, D (1999) Convolution kernels on discrete structures. Tech. Rep. UCS-CRL-99-10, University of California at Santa Cruz."},{"issue":"1","key":"195_CR42","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1093\/bioinformatics\/17.1.107","volume":"17","author":"C Helma","year":"2001","unstructured":"Helma, C, King RD, Kramer S, Srinivasan A (2001) The predictive toxicology challenge 2000\u20132001. Bioinformatics 17(1):107\u2013108.","journal-title":"Bioinformatics"},{"key":"195_CR43","doi-asserted-by":"publisher","unstructured":"Hermansson, L, Kerola T, Johansson F, Jethava V, Dubhashi D (2013) Entity disambiguation in anonymized graphs using graph kernels In: ACM International Conference on Information & Knowledge Management, 1037\u20131046. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/2505515.2505565.","DOI":"10.1145\/2505515.2505565"},{"key":"195_CR44","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-319-24282-8_8","volume-title":"Discovery Science","author":"Linus Hermansson","year":"2015","unstructured":"Hermansson, L, Johansson FD, Watanabe O (2015) Generalized shortest path kernel on graphs In: Discovery Science: International Conference, 78\u201385. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-24282-8_8."},{"key":"195_CR45","doi-asserted-by":"publisher","unstructured":"Hido, S, Kashima H (2009) A linear-time graph kernel In: IEEE International Conference on Data Mining, 179\u2013188. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/icdm.2009.30.","DOI":"10.1109\/icdm.2009.30"},{"key":"195_CR46","doi-asserted-by":"publisher","unstructured":"Horv\u00e1th, T, G\u00e4rtner T, Wrobel S (2004) Cyclic pattern kernels for predictive graph mining In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 158\u2013167. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/1014052.1014072.","DOI":"10.1145\/1014052.1014072"},{"key":"195_CR47","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/s10618-009-0162-1","volume":"21","author":"T Horv\u00e1th","year":"2010","unstructured":"Horv\u00e1th, T, Ramon J, Wrobel S (2010) Frequent subgraph mining in outerplanar graphs. Data Min Knowl Discov 21:472\u2013508. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s10618-009-0162-1.","journal-title":"Data Min Knowl Discov"},{"key":"195_CR48","doi-asserted-by":"publisher","unstructured":"Jie, B, Liu M, Jiang X, Zhang D (2016) Sub-network based kernels for brain network classification In: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 622\u2013629. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/2975167.2985687.","DOI":"10.1145\/2975167.2985687"},{"key":"195_CR49","doi-asserted-by":"publisher","unstructured":"Johansson, FD, Dubhashi D (2015) Learning with similarity functions on graphs using matchings of geometric embeddings In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 467\u2013476. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/2783258.2783341.","DOI":"10.1145\/2783258.2783341"},{"key":"195_CR50","unstructured":"Johansson, FD, Jethava V, Dubhashi DP, Bhattacharyya C (2014) Global graph kernels using geometric embeddings In: International Conference on Machine Learning, 694\u2013702."},{"key":"195_CR51","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-23240-9_1","volume-title":"Modeling Decisions for Artificial Intelligence","author":"Fredrik D. Johansson","year":"2015","unstructured":"Johansson, FD, Frost O, Retzner C, Dubhashi D (2015) Classifying large graphs with differential privacy In: Modeling Decisions for Artificial Intelligence, 3\u201317.. Springer. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-23240-9_1."},{"issue":"1","key":"195_CR52","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1145\/1077464.1077476","volume":"1","author":"DS Johnson","year":"2005","unstructured":"Johnson, DS (2005) The NP-completeness column. ACM Trans Algorithms 1(1):160\u2013176. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/1077464.1077476.","journal-title":"ACM Trans Algorithms"},{"key":"195_CR53","doi-asserted-by":"publisher","unstructured":"Kang, U, Tong H, Sun J (2012) Fast random walk graph kernel In: SIAM International Conference on Data Mining, 828\u2013838. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1137\/1.9781611972825.71.","DOI":"10.1137\/1.9781611972825.71"},{"key":"195_CR54","unstructured":"Kashima, H, Tsuda K, Inokuchi A (2003) Marginalized kernels between labeled graphs In: International Conference on Machine Learning, 321\u2013328."},{"issue":"13","key":"195_CR55","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1021\/jm040835a","volume":"48","author":"J Kazius","year":"2005","unstructured":"Kazius, J, McGuire R, Bursi R (2005) Derivation and validation of toxicophores for mutagenicity prediction. J Med Chem 48(13):312\u2013320.","journal-title":"J Med Chem"},{"key":"195_CR56","unstructured":"Kersting, K, Kriege NM, Morris C, Mutzel P, Neumann M (2016) Benchmark data sets for graph kernels. https:\/\/2.zoppoz.workers.dev:443\/http\/graphkernels.cs.tu-dortmund.de."},{"key":"195_CR57","unstructured":"Kipf, TN, Welling M (2017) Semi-supervised classification with graph convolutional networks In: International Conference on Learning Representations."},{"key":"195_CR58","unstructured":"Kondor, R, Pan H (2016) The multiscale laplacian graph kernel In: Advances in Neural Information Processing Systems, 2982\u20132990."},{"key":"195_CR59","doi-asserted-by":"publisher","unstructured":"Kondor, R, Shervashidze N, Borgwardt KM (2009) The graphlet spectrum In: International Conference on Machine Learning, 529\u2013536. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/1553374.1553443.","DOI":"10.1145\/1553374.1553443"},{"key":"195_CR60","unstructured":"Kriege, N, Mutzel P (2012) Subgraph matching kernels for attributed graphs In: International Conference on Machine Learning."},{"key":"195_CR61","doi-asserted-by":"publisher","unstructured":"Kriege, N, Neumann M, Kersting K, Mutzel M (2014) Explicit versus implicit graph feature maps: A computational phase transition for walk kernels In: IEEE International Conference on Data Mining, 881\u2013886. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/icdm.2014.129.","DOI":"10.1109\/icdm.2014.129"},{"key":"195_CR62","unstructured":"Kriege, NM (2015) Comparing graphs: Algorithms & applications. Phd thesis, TU Dortmund University."},{"key":"195_CR63","unstructured":"Kriege, NM (2019) Deep Weisfeiler-Lehman assignment kernels via multiple kernel learning In: 27th European Symposium on Artificial Neural Networks, ESANN 2019."},{"key":"195_CR64","unstructured":"Kriege, NM, Giscard PL, Wilson RC (2016) On valid optimal assignment kernels and applications to graph classification In: Advances in Neural Information Processing Systems, 1615\u20131623."},{"issue":"6","key":"195_CR65","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1007\/s10618-019-00652-0","volume":"33","author":"NM Kriege","year":"2019","unstructured":"Kriege, NM, Neumann M, Morris C, Kersting K, Mutzel P (2019) A unifying view of explicit and implicit feature maps of graph kernels. Data Mining and Knowledge Discovery 33(6):1505\u20131547. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s10618-019-00652-0.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"195_CR66","doi-asserted-by":"publisher","unstructured":"Kriege, NM, Morris C, Rey A, Sohler C (2018) A property testing framework for the theoretical expressivity of graph kernels In: International Joint Conference on Artificial Intelligence, 2348\u20132354. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.24963\/ijcai.2018\/325.","DOI":"10.24963\/ijcai.2018\/325"},{"key":"195_CR67","doi-asserted-by":"publisher","unstructured":"Li, B, Zhu X, Chi L, Zhang C (2012) Nested subtree hash kernels for large-scale graph classification over streams In: IEEE International Conference on Data Mining, 399\u2013408. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/icdm.2012.101.","DOI":"10.1109\/icdm.2012.101"},{"key":"195_CR68","doi-asserted-by":"publisher","unstructured":"Li, L, Tong H, Xiao Y, Fan W (2015) Cheetah: Fast graph kernel tracking on dynamic graphs In: SIAM International Conference on Data Mining, 280\u2013288. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1137\/1.9781611974010.32.","DOI":"10.1137\/1.9781611974010.32"},{"key":"195_CR69","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/978-3-319-35122-3_21","volume-title":"Lecture Notes in Computer Science","author":"Wenchao Li","year":"2016","unstructured":"Li, W, Saidi H, Sanchez H, Sch\u00e4f M, Schweitzer P (2016) Detecting similar programs via the Weisfeiler-Leman graph kernel In: International Conference on Software Reuse, 315\u2013330. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-35122-3_21."},{"issue":"99","key":"195_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2015.2477830","volume":"PP","author":"G Loosli","year":"2015","unstructured":"Loosli, G, Canu S, Ong CS (2015) Learning svm in krein spaces. IEEE Trans Pattern Anal Mach Intell PP(99):1\u20131. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/TPAMI.2015.2477830.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"195_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIT.1979.1055985","volume":"25","author":"L Lov\u00e1sz","year":"2006","unstructured":"Lov\u00e1sz, L (2006) On the shannon capacity of a graph. IEEE Trans Inf Theory 25(1):1\u20137.","journal-title":"IEEE Trans Inf Theory"},{"issue":"Nov","key":"195_CR72","first-page":"2579","volume":"9","author":"G Hinton","year":"2008","unstructured":"Maaten Lvd, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(Nov):2579\u20132605.","journal-title":"J Mach Learn Res"},{"issue":"1","key":"195_CR73","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-008-5086-2","volume":"75","author":"P Mah\u00e9","year":"2009","unstructured":"Mah\u00e9, P, Vert JP (2009) Graph kernels based on tree patterns for molecules. Mach Learn 75(1):3\u201335.","journal-title":"Mach Learn"},{"key":"195_CR74","doi-asserted-by":"publisher","unstructured":"Mah\u00e9, P, Ueda N, Akutsu T, Perret JL, Vert JP (2004) Extensions of marginalized graph kernels In: International Conference on Machine Learning, 552\u2013559. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/1015330.1015446.","DOI":"10.1145\/1015330.1015446"},{"issue":"4","key":"195_CR75","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1021\/ci050039t","volume":"45","author":"P Mah\u00e9","year":"2005","unstructured":"Mah\u00e9, P, Ueda N, Akutsu T, Perret JL, Vert JP (2005) Graph kernels for molecular structure-activity relationship analysis with support vector machines. J Chem Inf Model 45(4):939\u2013951.","journal-title":"J Chem Inf Model"},{"issue":"5","key":"195_CR76","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1021\/ci060138m","volume":"46","author":"P Mah\u00e9","year":"2006","unstructured":"Mah\u00e9, P, Ralaivola L, Stoven V, Vert JP (2006) The pharmacophore kernel for virtual screening with support vector machines. J Chem Inf Model 46(5):2003\u20132014.","journal-title":"J Chem Inf Model"},{"key":"195_CR77","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-319-46672-9_25","volume-title":"Neural Information Processing","author":"Carlo M. Massimo","year":"2016","unstructured":"Massimo, CM, Navarin N, Sperduti A (2016) Hyper-parameter tuning for graph kernels via multiple kernel learning In: Advances in Neural Information Processing, 214\u2013223. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-46672-9_25."},{"issue":"0","key":"195_CR78","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.jsc.2013.09.003","volume":"60","author":"BD McKay","year":"2014","unstructured":"McKay, BD, Piperno A (2014) Practical graph isomorphism, II. J Symb Comput 60(0):94\u2013112. doi:10.1016\/j.jsc.2013.09.003.","journal-title":"J Symb Comput"},{"issue":"5","key":"195_CR79","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1021\/ci049613b","volume":"45","author":"C Merkwirth","year":"2005","unstructured":"Merkwirth, C, Lengauer T (2005) Automatic generation of complementary descriptors with molecular graph networks. J Chem Inf Model 45(5):1159\u20131168.","journal-title":"J Chem Inf Model"},{"key":"195_CR80","unstructured":"Mikolov, T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space In: 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings. https:\/\/2.zoppoz.workers.dev:443\/https\/dblp.org\/rec\/bib\/journals\/corr\/abs-1301-3781."},{"key":"195_CR81","unstructured":"Mohri, M, Rostamizadeh A, Talwalkar A (2012) Foundations of Machine Learning. MIT Press."},{"key":"195_CR82","doi-asserted-by":"publisher","unstructured":"Morris, C, Kriege NM, Kersting K, Mutzel P (2016) Faster kernel for graphs with continuous attributes via hashing In: IEEE International Conference on Data Mining, 1095\u20131100. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/icdm.2016.0142.","DOI":"10.1109\/icdm.2016.0142"},{"key":"195_CR83","doi-asserted-by":"crossref","unstructured":"Morris, C, Kersting K, Mutzel P (2017) Glocalized Weisfeiler-Lehman kernel: Global-local feature maps of graphs In: IEEE International Conference on Data Mining.","DOI":"10.1109\/ICDM.2017.42"},{"key":"195_CR84","doi-asserted-by":"publisher","first-page":"4602","DOI":"10.1609\/aaai.v33i01.33014602","volume":"33","author":"Christopher Morris","year":"2019","unstructured":"Morris, C, Ritzert M, Fey M, Hamilton WL, Lenssen JE, Rattan G, Grohe M (2019) Weisfeiler and Leman go neural: Higher-order graph neural networks In: AAAI Conference on Artificial Intelligence, TBD. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1609\/aaai.v33i01.33014602.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"195_CR85","unstructured":"Neumann, M (2015) Learning with graphs using kernels from propagated information. Phd thesis, University of Bonn."},{"key":"195_CR86","unstructured":"Neumann, M (2016) Propagation kernel (code). https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/marionmari\/propagation_kernels.git."},{"key":"195_CR87","unstructured":"Neumann, M, Moreno P, Antanas L, Garnett R, Kersting K (2013) Graph kernels for object category prediction in task\u2013dependent robot grasping. In: Adamic L, Getoor L, Huang B, Leskovec J, McAuley J (eds)Working Notes of the International Workshop on Mining and Learning with Graphs at KDD 2013, Chicago."},{"issue":"2","key":"195_CR88","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s10994-015-5517-9","volume":"102","author":"M Neumann","year":"2016","unstructured":"Neumann, M, Garnett R, Bauckhage C, Kersting K (2016) Propagation kernels: Efficient graph kernels from propagated information. Mach Learn 102(2):209\u2013245.","journal-title":"Mach Learn"},{"key":"195_CR89","unstructured":"Nikolentzos, G (2016) Pyramid match kernel. https:\/\/2.zoppoz.workers.dev:443\/http\/www.db-net.aueb.gr\/nikolentzos\/code\/matchingnodes.zip."},{"key":"195_CR90","doi-asserted-by":"publisher","unstructured":"Nikolentzos, G, Vazirgiannis M (2018) Enhancing graph kernels via successive embeddings In: ACM International Conference on Information and Knowledge Management, 1583\u20131586. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3269206.3269289.","DOI":"10.1145\/3269206.3269289"},{"key":"195_CR91","doi-asserted-by":"publisher","unstructured":"Nikolentzos, G, Meladianos P, Rousseau F, Stavrakas Y, Vazirgiannis M (2017a) Shortest-path graph kernels for document similarity In: Empirical Methods in Natural Language Processing, 1890\u20131900. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.18653\/v1\/d17-1202.","DOI":"10.18653\/v1\/d17-1202"},{"key":"195_CR92","doi-asserted-by":"crossref","unstructured":"Nikolentzos, G, Meladianos P, Vazirgiannis M (2017b) Matching node embeddings for graph similarity In: AAAI Conference on Artificial Intelligence, 2429\u20132435.","DOI":"10.1609\/aaai.v31i1.10839"},{"key":"195_CR93","doi-asserted-by":"publisher","unstructured":"Nikolentzos, G, Meladianos P, Limnios S, Vazirgiannis M (2018) A degeneracy framework for graph similarity In: International Joint Conference on Artificial Intelligenc, ijcai.org, 2595\u20132601. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.24963\/ijcai.2018\/360.","DOI":"10.24963\/ijcai.2018\/360"},{"issue":"Supplement C","key":"195_CR94","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.neucom.2017.02.088","volume":"268","author":"L Oneto","year":"2017","unstructured":"Oneto, L, Navarin N, Donini M, Sperduti A, Aiolli F, Anguita D (2017) Measuring the expressivity of graph kernels through statistical learning theory. Neurocomputing 268(Supplement C):4\u201316.","journal-title":"Neurocomputing"},{"key":"195_CR95","unstructured":"Orsini, F, Frasconi P, De Raedt L (2015) Graph invariant kernels In: International Joint Conference on Artificial Intelligence, 3756\u20133762."},{"key":"195_CR96","unstructured":"Pachauri, D, Kondor R, Singh V (2013) Solving the multi-way matching problem by permutation synchronization In: Advances in Neural Information Processing Systems, 1860\u20131868."},{"issue":"8","key":"195_CR97","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1016\/j.neunet.2005.07.009","volume":"18","author":"L Ralaivola","year":"2005","unstructured":"Ralaivola, L, Swamidass SJ, Saigo H, Baldi P (2005) Graph kernels for chemical informatics. Neural Netw 18(8):1093\u20131110. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.neunet.2005.07.009. Neural Networks and Kernel Methods for Structured Domains.","journal-title":"Neural Netw"},{"issue":"10","key":"195_CR98","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/PL00013304","volume":"37","author":"J Ramon","year":"2001","unstructured":"Ramon, J, Bruynooghe M (2001) A polynomial time computable metric between point sets. Acta Inform 37(10):765\u2013780. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/PL00013304.","journal-title":"Acta Inform"},{"key":"195_CR99","unstructured":"Ramon, J, G\u00e4rtner T (2003) Expressivity versus efficiency of graph kernels In: International Workshop on Mining Graphs, Trees and Sequences, 65\u201374."},{"key":"195_CR100","doi-asserted-by":"crossref","unstructured":"Rasmussen, CE (2004) Gaussian processes in machine learning In: Advanced lectures on machine learning, 63\u201371.. Springer.","DOI":"10.1007\/978-3-540-28650-9_4"},{"key":"195_CR101","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-540-89689-0_33","volume-title":"Lecture Notes in Computer Science","author":"Kaspar Riesen","year":"2008","unstructured":"Riesen, K, Bunke H (2008) Iam graph database repository for graph based pattern recognition and machine learning In: Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, 287\u2013297. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-540-89689-0_33."},{"issue":"5","key":"195_CR102","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers, D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742\u2013754. doi:10.1021\/ci100050t.","journal-title":"J Chem Inf Model"},{"key":"195_CR103","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-24261-3_12","volume-title":"Similarity-Based Pattern Recognition","author":"Michele Schiavinato","year":"2015","unstructured":"Schiavinato, M, Gasparetto A, Torsello A (2015) Transitive assignment kernels for structural classification In: Similarity-Based Pattern Recognition: Third International Workshop, 146\u2013159. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-24261-3_12."},{"key":"195_CR104","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B, Smola AJ (2001) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge."},{"key":"195_CR105","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B, Smola A, M\u00fcller KR (1997) Kernel principal component analysis In: International Conference on Artificial Neural Networks, 583\u2013588.. Springer.","DOI":"10.1007\/BFb0020217"},{"key":"195_CR106","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1093\/nar\/gkh081","volume":"32","author":"I Schomburg","year":"2004","unstructured":"Schomburg, I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, Schomburg D (2004) Brenda, the enzyme database: updates and major new developments. Nucleic Acids Res 32:431\u2013433. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1093\/nar\/gkh081.","journal-title":"Nucleic Acids Res"},{"key":"195_CR107","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel Methods for Pattern Analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor, J, Cristianini N (2004) Kernel Methods for Pattern Analysis. Cambridge University Press, New York."},{"key":"195_CR108","unstructured":"Shervashidze, N (2012) Scalable graph kernels. Phd thesis."},{"key":"195_CR109","unstructured":"Shervashidze, N, Vishwanathan SVN, Petri TH, Mehlhorn K, Borgwardt KM (2009) Efficient graphlet kernels for large graph comparison In: International Conference on Artificial Intelligence and Statistics, 488\u2013495."},{"key":"195_CR110","first-page":"2539","volume":"12","author":"N Shervashidze","year":"2011","unstructured":"Shervashidze, N, Schweitzer P, van Leeuwen EJ, Mehlhorn K, Borgwardt KM (2011) Weisfeiler-Lehman graph kernels. J Mach Learn Res 12:2539\u20132561.","journal-title":"J Mach Learn Res"},{"key":"195_CR111","doi-asserted-by":"publisher","unstructured":"Shin, K, Kuboyama T (2008) A generalization of haussler\u2019s convolution kernel: mapping kernel In: International conference on Machine learning, 944\u2013951.. ACM. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/1390156.1390275.","DOI":"10.1145\/1390156.1390275"},{"key":"195_CR112","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-3324-9","volume-title":"Density Estimation for Statistics and Data Analysis","author":"BW Silverman","year":"1986","unstructured":"Silverman, BW (1986) Density Estimation for Statistics and Data Analysis. Chapman & Hall\/CRC, London."},{"key":"195_CR113","doi-asserted-by":"publisher","unstructured":"Su, Y, Han F, Harang RE, Yan X (2016) A fast kernel for attributed graphs In: SIAM International Conference on Data Mining, 486\u2013494. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1137\/1.9781611974348.55.","DOI":"10.1137\/1.9781611974348.55"},{"key":"195_CR114","unstructured":"Sugiyama, M, Borgwardt KM (2015) Halting in random walk kernels In: Advances in Neural Information Processing Systems, 1639\u20131647."},{"issue":"6","key":"195_CR115","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1021\/ci034143r","volume":"43","author":"JJ Sutherland","year":"2003","unstructured":"Sutherland, JJ, O\u2019Brien LA, Weaver DF (2003) Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships. J Chem Inf Comput Sci 43(6):1906\u20131915. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1021\/ci034143r.","journal-title":"J Chem Inf Comput Sci"},{"issue":"Suppl 1","key":"195_CR116","doi-asserted-by":"publisher","first-page":"i359","DOI":"10.1093\/bioinformatics\/bti1055","volume":"21","author":"SJ Swamidass","year":"2005","unstructured":"Swamidass, SJ, Chen J, Bruand J, Phung P, Ralaivola L, Baldi P (2005) Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. Bioinformatics 21(Suppl 1):i359\u2013i368.","journal-title":"Bioinformatics"},{"issue":"8","key":"195_CR117","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0104586","volume":"9","author":"S Takerkart","year":"2014","unstructured":"Takerkart, S, Auzias G, Thirion B, Ralaivola L (2014) Graph-based inter-subject pattern analysis of fmri data. PLoS ONE 9(8):1\u201314. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1371\/journal.pone.0104586.","journal-title":"PLoS ONE"},{"key":"195_CR118","unstructured":"Tox, 21 Data Challenge (2014). https:\/\/2.zoppoz.workers.dev:443\/https\/tripod.nih.gov\/tox21\/challenge\/data.jsp."},{"key":"195_CR119","doi-asserted-by":"publisher","unstructured":"Vega-Pons, S, Avesani P (2013) Brain decoding via graph kernels In: Proceedings of the 2013 International Workshop on Pattern Recognition in Neuroimaging, IEEE Computer Society, Washington, DC, USA, PRNI \u201913, 136\u2013139. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/PRNI.2013.43.","DOI":"10.1109\/PRNI.2013.43"},{"key":"195_CR120","doi-asserted-by":"publisher","unstructured":"Vega-Pons, S, Avesani P, Andric M, Hasson U (2014) Classification of inter-subject fmri data based on graph kernels In: International Workshop on Pattern Recognition in Neuroimaging, 1\u20134. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/PRNI.2014.6858549.","DOI":"10.1109\/PRNI.2014.6858549"},{"key":"195_CR121","unstructured":"Vert, J (2008) The optimal assignment kernel is not positive definite. CoRR:abs\/0801.4061. https:\/\/2.zoppoz.workers.dev:443\/http\/arxiv.org\/abs\/0801.4061."},{"key":"195_CR122","first-page":"1201","volume":"11","author":"SVN Vishwanathan","year":"2010","unstructured":"Vishwanathan, SVN, Schraudolph NN, Kondor R, Borgwardt KM (2010) Graph kernels. J Mach Learn Res 11:1201\u20131242.","journal-title":"J Mach Learn Res"},{"issue":"3","key":"195_CR123","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s10115-007-0103-5","volume":"14","author":"N Wale","year":"2008","unstructured":"Wale, N, Watson IA, Karypis G (2008) Comparison of descriptor spaces for chemical compound retrieval and classification. Knowl Inf Syst 14(3):347\u2013375.","journal-title":"Knowl Inf Syst"},{"key":"195_CR124","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1007\/978-3-319-49055-7_20","volume-title":"Lecture Notes in Computer Science","author":"Jianjia Wang","year":"2016","unstructured":"Wang, J, Wilson RC, Hancock ER (2016) fmri activation network analysis using bose-einstein entropy In: Robles-Kelly A, Loog M, Biggio B, Escolano F, Wilson R (eds) Structural, Syntactic, and Statistical Pattern Recognition, 218\u2013228.. Springer International Publishing, Cham. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-49055-7_20."},{"issue":"1","key":"195_CR125","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1002\/qsar.19860050105","volume":"5","author":"P Willett","year":"1986","unstructured":"Willett, P, Winterman V (1986) A comparison of some measures for the determination of inter-molecular structural similarity measures of inter-molecular structural similarity. Quant Struct-Act Relationsh 5(1):18\u201325. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/qsar.19860050105.","journal-title":"Quant Struct-Act Relationsh"},{"key":"195_CR126","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/978-3-642-13672-6_37","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Adam Wo\u017anica","year":"2010","unstructured":"Wo\u017anica, A, Kalousis A, Hilario M (2010) Adaptive matching based kernels for labelled graphs In: Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, vol 6119, 374\u2013385. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-642-13672-6_37."},{"key":"195_CR127","doi-asserted-by":"publisher","unstructured":"Wu, B, Yuan C, Hu W (2014) Human action recognition based on context-dependent graph kernels In: IEEE Conference on Computer Vision and Pattern Recognition, 2609\u20132616. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/CVPR.2014.334.","DOI":"10.1109\/CVPR.2014.334"},{"key":"195_CR128","first-page":"376","volume":"14","author":"A Yamaguchi","year":"2003","unstructured":"Yamaguchi, A, Aoki KF, Mamitsuka H (2003) Graph complexity of chemical compounds in biological pathways. Genome Inf 14:376\u2013377.","journal-title":"Genome Inf"},{"key":"195_CR129","doi-asserted-by":"crossref","unstructured":"Yanardag, P (2015) Deep graph kernels (code). https:\/\/2.zoppoz.workers.dev:443\/http\/www.mit.edu\/pinary\/kdd\/DEEP_GRAPH_KERNELS_CODE.tar.gz.","DOI":"10.1145\/2783258.2783417"},{"key":"195_CR130","doi-asserted-by":"publisher","unstructured":"Yanardag, P, Vishwanathan SVN (2015a) Deep graph kernels In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1365\u20131374. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/2783258.2783417.","DOI":"10.1145\/2783258.2783417"},{"key":"195_CR131","unstructured":"Yanardag, P, Vishwanathan SVN (2015b) A structural smoothing framework for robust graph comparison In: Advances in Neural Information Processing Systems, 2134\u20132142."},{"issue":"12","key":"195_CR132","doi-asserted-by":"publisher","first-page":"984","DOI":"10.3390\/e20120984","volume":"20","author":"Yi Zhang","year":"2018","unstructured":"Zhang, Y, Wang L, Wang L (2018a) A comprehensive evaluation of graph kernels for unattributed graphs. Entropy 20(12):984.","journal-title":"Entropy"},{"key":"195_CR133","unstructured":"Zhang, Z, Wang M, Xiang Y, Huang Y, Nehorai A (2018b) Retgk: Graph kernels based on return probabilities of random walks In: Advances in Neural Information Processing Systems, 3964\u20133974."}],"container-title":["Applied Network Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/content\/pdf\/10.1007\/s41109-019-0195-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/article\/10.1007\/s41109-019-0195-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/content\/pdf\/10.1007\/s41109-019-0195-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T03:25:50Z","timestamp":1722309950000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/appliednetsci.springeropen.com\/articles\/10.1007\/s41109-019-0195-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,14]]},"references-count":133,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["195"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s41109-019-0195-3","relation":{},"ISSN":["2364-8228"],"issn-type":[{"value":"2364-8228","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,14]]},"assertion":[{"value":"21 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}