<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Link Prediction in Complex Networks: A Survey · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1010.0725/</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/1010.0725/feed.xml" rel="self" type="application/rss+xml" /><item><title>Link Prediction in Complex Networks: A Survey — mentioned by thashim</title><link>https://news.ycombinator.com/item?id=9063088</link><guid isPermaLink="false">urn:hackerpapers:mention:1010.0725:9063088</guid><pubDate>Tue, 17 Feb 2015 16:24:38 GMT</pubDate><description>&lt;p&gt;The last sentence of your post is known as &amp;#x27;link-prediction&amp;#x27; in the networks literature. [1] is a pretty comprehensive survey from the networks perspective. In many cases this reduces to learning pairwise distances between vertices. The network translation view is interesting, though one caution would be that you can have two graphs constructed on identical points, but with a different graph construction technique (say k-nearest neighbor vs fixed eps ball graph) and it is unclear if you should say the two graphs are the same (since they share the same latent coordinates) or if they are…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1010.0725/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.soc-ph</category></item></channel></rss>
