{"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/dfnets-spectral-cnns-for-graphs-with-feedback","title":"DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters","arxiv_id":"1910.10866","date":"2019-10-24","proceeding":"NeurIPS 2019 12","authors":["Asiri Wijesinghe","Qing Wang"],"abstract":"We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets). This model is incorporated with a robust class of spectral graph filters, called feedback-looped filters, to provide better localization on vertices, while still attaining fast convergence and linear memory requirements. Theoretically, feedback-looped filters can guarantee convergence w.r.t. a specified error bound, and be applied universally to any graph without knowing its structure. Furthermore, the propagation rule of this model can diversify features from the preceding layers to produce strong gradient flows. We have evaluated our model using two benchmark tasks: semi-supervised document classification on citation networks and semi-supervised entity classification on a knowledge graph. The experimental results show that our model considerably outperforms the state-of-the-art methods in both benchmark tasks over all datasets.","url_abs":"https://arxiv.org/abs/1910.10866v5","url_pdf":"https://arxiv.org/pdf/1910.10866v5.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":"dfnets-spectral-cnns-for-graphs-with-feedback","repo_url":"https://github.com/wokas36/DFNets","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-citeseer","task":"Node Classification","dataset":"Citeseer","model":"DFNet-ATT","rank_in_archive_order":25,"of":71,"metrics":{"Accuracy":"74.7 ± 0.4"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-cora","task":"Node Classification","dataset":"Cora","model":"DFNet-ATT","rank_in_archive_order":20,"of":73,"metrics":{"Accuracy":"86% ± 0.4%"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-nell","task":"Node Classification","dataset":"NELL","model":"DFNet-ATT","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"68.8 ± 0.3"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-pubmed","task":"Node Classification","dataset":"Pubmed","model":"DFNet-ATT","rank_in_archive_order":23,"of":70,"metrics":{"Accuracy":"85.2 ± 0.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.10866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.10866"}},"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. 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