<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Benefits of Bias: Towards Better Characterization of Network Sampling · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1109.3911/</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/1109.3911/feed.xml" rel="self" type="application/rss+xml" /><item><title>Benefits of Bias: Towards Better Characterization of Network Sampling — mentioned by romwell</title><link>https://news.ycombinator.com/item?id=33782377</link><guid isPermaLink="false">urn:hackerpapers:mention:1109.3911:33782377</guid><pubDate>Tue, 29 Nov 2022 01:11:34 GMT</pubDate><description>&lt;p&gt;True. My comment was to highlight how wrong my initial intuition was. Looking around, this article[1] by Arun Maiya and Tanya Berger-Wolf explores this in the context of social networks. Fascinating stuff! [1] https://arxiv.org/pdf/1109.3911.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1109.3911/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.SI</category></item></channel></rss>
