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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Semi-Supervised Graph Classification: A Hierarchical Graph Perspective · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1904.05003/</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/1904.05003/feed.xml" rel="self" type="application/rss+xml" /><item><title>Semi-Supervised Graph Classification: A Hierarchical Graph Perspective — mentioned by arbol</title><link>https://news.ycombinator.com/item?id=22382463</link><guid isPermaLink="false">urn:hackerpapers:mention:1904.05003:22382463</guid><pubDate>Fri, 21 Feb 2020 11:49:54 GMT</pubDate><description>&lt;p&gt;I may be wrong, but I think the OP is asking if this method can be used to identify related graphs based on similarity. Take, for example, a graph of someone&amp;#x27;s movements between locations on a daily basis. Although not exactly the same, there may be similarities that reveal the person the graph relates to. See https://arxiv.org/pdf/1904.05003 For example&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1904.05003/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
