{"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/elastic-graph-neural-networks","title":"Elastic Graph Neural Networks","arxiv_id":"2107.06996","date":"2021-07-05","proceeding":null,"authors":["Xiaorui Liu","Wei Jin","Yao Ma","Yaxin Li","Hua Liu","Yiqi Wang","Ming Yan","Jiliang Tang"],"abstract":"While many existing graph neural networks (GNNs) have been proven to perform $\\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\\ell_1$-based graph smoothing. As a result, we introduce a family of GNNs (Elastic GNNs) based on $\\ell_1$ and $\\ell_2$-based graph smoothing. In particular, we propose a novel and general message passing scheme into GNNs. This message passing algorithm is not only friendly to back-propagation training but also achieves the desired smoothing properties with a theoretical convergence guarantee. Experiments on semi-supervised learning tasks demonstrate that the proposed Elastic GNNs obtain better adaptivity on benchmark datasets and are significantly robust to graph adversarial attacks. The implementation of Elastic GNNs is available at \\url{https://github.com/lxiaorui/ElasticGNN}.","url_abs":"https://arxiv.org/abs/2107.06996v1","url_pdf":"https://arxiv.org/pdf/2107.06996v1.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":"elastic-graph-neural-networks","repo_url":"https://github.com/lxiaorui/ElasticGNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.06996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.06996"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/lxiaorui/ElasticGNN","reach":null}],"summary":{"ran":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"022a9599aba048ef","entry":"ElasticGNN","repo":"lxiaorui/ElasticGNN","repo_kind":"official","path":"code/elasticgnn.py","file_url":"https://github.com/lxiaorui/ElasticGNN/blob/HEAD/code/elasticgnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"022a9599aba048ef"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}