{"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/graph-laplacian-regularized-graph","title":"Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning","arxiv_id":"1809.09839","date":"2018-09-26","proceeding":null,"authors":["Bo Jiang","Doudou Lin"],"abstract":"Recently, graph convolutional network (GCN) has been widely used for\nsemi-supervised classification and deep feature representation on\ngraph-structured data. However, existing GCN generally fails to consider the\nlocal invariance constraint in learning and representation process. That is, if\ntwo data points Xi and Xj are close in the intrinsic geometry of the data\ndistribution, then their labels/representations should also be close to each\nother. This is known as local invariance assumption which plays an essential\nrole in the development of various kinds of traditional algorithms, such as\ndimensionality reduction and semi-supervised learning, in machine learning\narea. To overcome this limitation, we introduce a graph Laplacian GCN (gLGCN)\napproach for graph data representation and semi-supervised classification. The\nproposed gLGCN model is capable of encoding both graph structure and node\nfeatures together while maintains the local invariance constraint naturally for\nrobust data representation and semi-supervised classification. Experiments show\nthe benefit of the benefits the proposed gLGCN network.","url_abs":"http://arxiv.org/abs/1809.09839v1","url_pdf":"http://arxiv.org/pdf/1809.09839v1.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":"graph-laplacian-regularized-graph","repo_url":"https://github.com/abhilash1910/Deep-Graph-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"graph-laplacian-regularized-graph","repo_url":"https://github.com/abhilash1910/SpectralEmbeddings","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.09839","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}