{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T19:43:41Z","timestamp":1776195821966,"version":"3.50.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2017,9,23]],"date-time":"2017-09-23T00:00:00Z","timestamp":1506124800000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["634541"],"award-info":[{"award-number":["634541"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C\u2009++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R and Python packages including source code are available at https:\/\/CRAN.R-project.org\/package=graphkernels and https:\/\/pypi.python.org\/pypi\/graphkernels.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available online at Bioinformatics.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx602","type":"journal-article","created":{"date-parts":[[2017,9,19]],"date-time":"2017-09-19T11:37:20Z","timestamp":1505821040000},"page":"530-532","source":"Crossref","is-referenced-by-count":31,"title":["graphkernels: R and Python packages for graph comparison"],"prefix":"10.1093","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5907-9831","authenticated-orcid":false,"given":"Mahito","family":"Sugiyama","sequence":"first","affiliation":[{"name":"National Institute of Informatics, Chiyoda-ku, Tokyo, Japan"},{"name":"JST PRESTO, Kawaguchi, Saitama, Japan"}]},{"given":"M Elisabetta","family":"Ghisu","sequence":"additional","affiliation":[{"name":"D-BSSE, ETH Z\u00fcrich, Switzerland"},{"name":"Swiss Institute of Bioinformatics, Basel, Switzerland"}]},{"given":"Felipe","family":"Llinares-L\u00f3pez","sequence":"additional","affiliation":[{"name":"D-BSSE, ETH Z\u00fcrich, Switzerland"},{"name":"Swiss Institute of Bioinformatics, Basel, Switzerland"}]},{"given":"Karsten","family":"Borgwardt","sequence":"additional","affiliation":[{"name":"D-BSSE, ETH Z\u00fcrich, Switzerland"},{"name":"Swiss Institute of Bioinformatics, Basel, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2017,9,22]]},"reference":[{"key":"2023012712315436900_btx602-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v052.i05","article-title":"Fast and elegant numerical linear algebra using the RcppEigen package","volume":"52","author":"Bates","year":"2013","journal-title":"J. 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Biol"},{"key":"2023012712315436900_btx602-B10","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/978-3-540-45167-9_11","article-title":"On graph kernels: Hardness results and efficient alternatives","author":"G\u00e4rtner","year":"2003","journal-title":"Learning Theory and Kernel Machines"},{"key":"2023012712315436900_btx602-B11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"kernlab\u2013an S4 package for kernel methods in R","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw"},{"key":"2023012712315436900_btx602-B12","first-page":"321","author":"Kashima","year":"2003"},{"key":"2023012712315436900_btx602-B13","author":"Shervashidze","year":"2009"},{"key":"2023012712315436900_btx602-B14","first-page":"2359","article-title":"Weisfeiler-Lehman graph kernels","volume":"12","author":"Shervashidze","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"2023012712315436900_btx602-B15","first-page":"1639","article-title":"Halting in random walk kernels","author":"Sugiyama","year":"2015","journal-title":"Advances in Neural Information Processing Systems 28"},{"key":"2023012712315436900_btx602-B16","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.drudis.2012.07.016","article-title":"Graph mining: procedure, application to drug discovery and recent advances","volume":"18","author":"Takigawa","year":"2013","journal-title":"Drug Discov. Today"},{"key":"2023012712315436900_btx602-B17","first-page":"1201","article-title":"Graph kernels","volume":"11","author":"Vishwanathan","year":"2010","journal-title":"J. Mach. Learn. 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