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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Expressive Power of Graph Neural Networks: A Survey · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2308.08235/</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/2308.08235/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Expressive Power of Graph Neural Networks: A Survey — mentioned by stefanpie</title><link>https://news.ycombinator.com/item?id=37572418</link><guid isPermaLink="false">urn:hackerpapers:mention:2308.08235:37572418</guid><pubDate>Tue, 19 Sep 2023 16:33:24 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;ve work with GNNs. I have not explicitly looked into this, but in general, the dynamics of GNNs are really interesting and there are many different lenses in which one can study the behavior of GNNs and find interesting takeaways. - Explicit Message Passing: https://arxiv.org/abs/2202.11097 - Algorithmic Reasoning: https://arxiv.org/abs/2203.15544 - Diffusion: https://proceedings.mlr.press/v139/chamberlain21a.html - Spectral: https://arxiv.org/abs/2010.02863 - Sparsity: https://arxiv.org/abs/2211.15335 - Training Tricks: https://arxiv.org/abs/2108.10521 - Interaction Networks / Physical…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2308.08235/"&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>
