<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>GRAND: Graph Neural Diffusion · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2106.10934/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2106.10934/feed.xml" rel="self" type="application/rss+xml" /><item><title>GRAND: Graph Neural Diffusion — mentioned by ssivark</title><link>https://news.ycombinator.com/item?id=31320903</link><guid isPermaLink="false">urn:hackerpapers:mention:2106.10934:31320903</guid><pubDate>Mon, 09 May 2022 22:50:34 GMT</pubDate><description>&lt;p&gt;From a quick glance, the blog post seems to be based on the following paper involving the same author: https://arxiv.org/abs/2106.10934 The point of the post is how graph NNs are limited in capability, so I’m not sure graph NN surveys are the best references (sibling comment) for the main punchline, though they might certainly contain many important details.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2106.10934/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
