https://kevinng77.github.io/assets/%E7%AC%94%E8%AE%B0graghsage.html-115a3e0a.js

1const e=JSON.parse('{"key":"v-a86213d2","path":"/posts/notes/articles/%E7%AC%94%E8%AE%B0graghsage.html","title":"图网络模型（一）","lang":"zh-CN","frontmatter":{"title":"图网络模型（一）","date":"2021-08-24T00:00:00.000Z","author":"Kevin 吴嘉文","category":["知识笔记"],"tag":["图网络","论文笔记"],"mathjax":true,"toc":true,"comments":"笔记","description":"谈谈那些图网络模型 - Monet、EGCN、GraphSage 几何模型 (geometric models) 研究已经在图像处理、流形学习、信号处理等领域得到了广泛的关注。最近几年也有不少的基于 非欧几里德结构数据 深度学习研究。本文主要对 Monet、EGCN、GraphSage 等模型进行总结。","head":[["meta",{"property":"og:url","content":"http://antarina.tech/posts/notes/articles/%E7%AC%94%E8%AE%B0graghsage.html"}],["meta",{"property":"og:site_name","content":"记忆笔书"}],["meta",{"property":"og:title","content":"图网络模型（一）"}],["meta",{"property":"og:description","content":"谈谈那些图网络模型 - Monet、EGCN、GraphSage 几何模型 (geometric models) 研究已经在图像处理、流形学习、信号处理等领域得到了广泛的关注。最近几年也有不少的基于 非欧几里德结构数据 深度学习研究。本文主要对 Monet、EGCN、GraphSage 等模型进行总结。"}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"zh-CN"}],["meta",{"property":"og:updated_time","content":"2023-03-26T07:48:04.000Z"}],["meta",{"property":"article:author","content":"Kevin 吴嘉文"}],["meta",{"property":"article:tag","content":"图网络"}],["meta",{"property":"article:tag","content":"论文笔记"}],["meta",{"property":"article:published_time","content":"2021-08-24T00:00:00.000Z"}],["meta",{"property":"article:modified_time","content":"2023-03-26T07:48:04.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"图网络模型（一）\\",\\"image\\":[\\"\\"],\\"datePublished\\":\\"2021-08-24T00:00:00.000Z\\",\\"dateModified\\":\\"2023-03-26T07:48:04.000Z\\",\\"author\\":[{\\"@type\\":\\"Person\\",\\"name\\":\\"Kevin 吴嘉文\\"}]}"]]},"headers":[{"level":2,"title":"Monet","slug":"monet","link":"#monet","children":[]},{"level":2,"title":"EGNN","slug":"egnn","link":"#egnn","children":[]},{"level":2,"title":"GraghSage","slug":"graghsage","link":"#graghsage","children":[]},{"level":2,"title":"参考","slug":"参考","link":"#参考","children":[]}],"git":{"createdTime":1679816884000,"updatedTime":1679816884000,"contributors":[{"name":"kevinng77","email":"[email protected]","commits":1}]},"readingTime":{"minutes":10.18,"words":3054},"filePathRelative":"posts/notes/articles/笔记graghsage.md","localizedDate":"2021å¹´8月23日","excerpt":"<h1> 谈谈那些图网络模型 - Monet、EGCN、GraphSage</h1>\\n<blockquote>\\n<p>几何模型 (geometric models) 研究已经在图像处理、<a href=\\"https://zhuanlan.zhihu.com/p/44066392\\" target=\\"_blank\\" rel=\\"noopener noreferrer\\">流形学习</a>、信号处理等领域得到了广泛的关注。最近几年也有不少的基于 <strong>非欧几里德结构数据</strong> 深度学习研究。本文主要对 Monet、EGCN、GraphSage 等模型进行总结。</p>\\n</blockquote>","autoDesc":true}');export{e as data};