{"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/kernel-graph-convolutional-neural-networks","title":"Kernel Graph Convolutional Neural Networks","arxiv_id":"1710.10689","date":"2017-10-29","proceeding":null,"authors":["Giannis Nikolentzos","Polykarpos Meladianos","Antoine Jean-Pierre Tixier","Konstantinos Skianis","Michalis Vazirgiannis"],"abstract":"Graph kernels have been successfully applied to many graph classification\nproblems. Typically, a kernel is first designed, and then an SVM classifier is\ntrained based on the features defined implicitly by this kernel. This two-stage\napproach decouples data representation from learning, which is suboptimal. On\nthe other hand, Convolutional Neural Networks (CNNs) have the capability to\nlearn their own features directly from the raw data during training.\nUnfortunately, they cannot handle irregular data such as graphs. We address\nthis challenge by using graph kernels to embed meaningful local neighborhoods\nof the graphs in a continuous vector space. A set of filters is then convolved\nwith these patches, pooled, and the output is then passed to a feedforward\nnetwork. With limited parameter tuning, our approach outperforms strong\nbaselines on 7 out of 10 benchmark datasets.","url_abs":"http://arxiv.org/abs/1710.10689v2","url_pdf":"http://arxiv.org/pdf/1710.10689v2.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":"kernel-graph-convolutional-neural-networks","repo_url":"https://github.com/giannisnik/cnn-graph-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1710.10689","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}