{"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/kong-kernels-for-ordered-neighborhood-graphs","title":"KONG: Kernels for ordered-neighborhood graphs","arxiv_id":"1805.10014","date":"2018-05-25","proceeding":"NeurIPS 2018 12","authors":["Moez Draief","Konstantin Kutzkov","Kevin Scaman","Milan Vojnovic"],"abstract":"We present novel graph kernels for graphs with node and edge labels that have\nordered neighborhoods, i.e. when neighbor nodes follow an order. Graphs with\nordered neighborhoods are a natural data representation for evolving graphs\nwhere edges are created over time, which induces an order. Combining\nconvolutional subgraph kernels and string kernels, we design new scalable\nalgorithms for generation of explicit graph feature maps using sketching\ntechniques. We obtain precise bounds for the approximation accuracy and\ncomputational complexity of the proposed approaches and demonstrate their\napplicability on real datasets. In particular, our experiments demonstrate that\nneighborhood ordering results in more informative features. For the special\ncase of general graphs, i.e. graphs without ordered neighborhoods, the new\ngraph kernels yield efficient and simple algorithms for the comparison of label\ndistributions between graphs.","url_abs":"http://arxiv.org/abs/1805.10014v2","url_pdf":"http://arxiv.org/pdf/1805.10014v2.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":"kong-kernels-for-ordered-neighborhood-graphs","repo_url":"https://github.com/kokiche/KONG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}