From Infogalactic: the planetary knowledge core
Jump to: navigation, search

EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed. As of 2011, Facebook has switched from using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account.[1]

EdgeRank was developed and implemented by Serkan Piantino.

Formula and factors

In 2010, a simplified version of the EdgeRank algorithm was presented as:

\sum_{\mathrm{edges\,}e} u_e w_e d_e


u_e is user affinity.
w_e is how the content is weighted.
d_e is a time-based decay parameter.
  • User Affinity: The User Affinity part of the algorithm in Facebook's EdgeRank looks at the relationship and proximity of the user and the content (post/status update).[1]
  • Content Weight: What action was taken by the user on the content.[1]
  • Time-Based Decay Parameter: New or old. Newer posts tend to hold a higher place than older posts.[1]

Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public.[2]

See also

  • PageRank, the ranking algorithm used by Google's search engine


  1. 1.0 1.1 1.2 1.3 McGee, Matt (Aug 16, 2013). "EdgeRank Is Dead: Facebook's News Feed Algorithm Now Has Close To 100K Weight Factors". Retrieved 28 May 2014.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  2. "EdgeRank: The Secret Sauce That Makes Facebook's News Feed Tick". Techcrunch. 2010-04-22. Retrieved 2012-12-08. External link in |publisher= (help)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>

External links