{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/propagation-kernels","title":"Propagation Kernels","arxiv_id":"1410.3314","date":"2014-10-13","proceeding":null,"authors":["Marion Neumann","Roman Garnett","Christian Bauckhage","Kristian Kersting"],"abstract":"We introduce propagation kernels, a general graph-kernel framework for\nefficiently measuring the similarity of structured data. Propagation kernels\nare based on monitoring how information spreads through a set of given graphs.\nThey leverage early-stage distributions from propagation schemes such as random\nwalks to capture structural information encoded in node labels, attributes, and\nedge information. This has two benefits. First, off-the-shelf propagation\nschemes can be used to naturally construct kernels for many graph types,\nincluding labeled, partially labeled, unlabeled, directed, and attributed\ngraphs. Second, by leveraging existing efficient and informative propagation\nschemes, propagation kernels can be considerably faster than state-of-the-art\napproaches without sacrificing predictive performance. We will also show that\nif the graphs at hand have a regular structure, for instance when modeling\nimage or video data, one can exploit this regularity to scale the kernel\ncomputation to large databases of graphs with thousands of nodes. We support\nour contributions by exhaustive experiments on a number of real-world graphs\nfrom a variety of application domains.","url_abs":"http://arxiv.org/abs/1410.3314v1","url_pdf":"http://arxiv.org/pdf/1410.3314v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"propagation-kernels","repo_url":"https://github.com/marionmari/propagation_kernels","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1410.3314","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}