<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Comprehend DeepWalk as Matrix Factorization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1501.00358/</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/1501.00358/feed.xml" rel="self" type="application/rss+xml" /><item><title>Comprehend DeepWalk as Matrix Factorization — mentioned by thashim</title><link>https://news.ycombinator.com/item?id=9062749</link><guid isPermaLink="false">urn:hackerpapers:mention:1501.00358:9062749</guid><pubDate>Tue, 17 Feb 2015 15:36:03 GMT</pubDate><description>&lt;p&gt;The connection between the random walk and embeddings has been characterized in [1] (disclosure: I am an author). It turns out embedding in this setting is exactly smarter page rank as you suggested, since PageRank recovers the underlying density structure of an embedding. I&amp;#x27;m not hopeful that this is much more powerful than a matrix representation, since if [2] is true, it&amp;#x27;s a matrix factorization of the random-walk marginal. It&amp;#x27;s probably more scalable than the linear algebra approach, but not more powerful theoretically or representationally. One interesting connection that is unexplored…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1501.00358/"&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>
