{"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/universal-inceptive-gnns-by-eliminating-the","title":"Universal Inceptive GNNs by Eliminating the Smoothness-generalization Dilemma","arxiv_id":"2412.09805","date":"2024-12-13","proceeding":null,"authors":["Ming Gu","Zhuonan Zheng","Sheng Zhou","Meihan Liu","Jiawei Chen","Tanyu Qiao","Liangcheng Li","Jiajun Bu"],"abstract":"Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, such as transaction and social net-works. However, their application is often hindered by the varyinghomophily levels across different orders of neighboring nodes, ne-cessitating separate model designs for homophilic and heterophilicgraphs. In this paper, we aim to develop a unified framework ca-pable of handling neighborhoods of various orders and homophilylevels. Through theoretical exploration, we identify a previouslyoverlooked architectural aspect in multi-hop learning: the cascadedependency, which leads to asmoothness-generalization dilemma.This dilemma significantly affects the learning process, especiallyin the context of high-order neighborhoods and heterophilic graphs.To resolve this issue, we propose an Inceptive Graph Neural Net-work (IGNN), a universal message-passing framework that replacesthe cascade dependency with an inceptive architecture. IGNN pro-vides independent representations for each hop, allowing personal-ized generalization capabilities, and captures neighborhood-wiserelationships to select appropriate receptive fields. Extensive ex-periments show that our IGNN outperforms 23 baseline methods,demonstrating superior performance on both homophilic and het-erophilic graphs, while also scaling efficiently to large graphs.","url_abs":"https://arxiv.org/abs/2412.09805v1","url_pdf":"https://arxiv.org/pdf/2412.09805v1.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":[],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2412.09805","atlas_url":"https://app.syntology.ai/?focus=2412.09805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09805"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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