EdgeRank- A Facebook’s newsfeed algorithm

Hello Everybody..!!, This is Ravi once again with an exciting topic “EdgeRank”. Facebook had over 1.3 billion active users as of June 2014.We all use Facebook. Have you ever think how facebook’s newsfeed works ? What is the reason behind priority of contents ? Which post should be shown us first, which not ? Why are some posts more successful than others on the news feed? Have you ?
No ??? Let’s know What is behind this…..

There is an algorithm which plays a crucial role behind this named as “EdgeRank”. EdgeRank is the Facebook algorithm that decides which articles should be displayed in a user’s News Feed.As of 2011, Facebook has stopped using this but a new which takes more than 100,000 factors into account in addition to EdgeRank’s three.
EdgeRank was described as:

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

where:

u_e

is user affinity

w_e

is how the content is weighted

d_e

is a time-based decay parameter.

Every action their friends take is a potential newsfeed story. Facebook calls these actions “Edges.” That means whenever a friend posts a status update, comments on another status update, tags a photo, joins a fan page, or respond to an event it generates an “Edge,” and a story about that Edge might show up in the user’s personal newsfeed. It would be annoying if the newsfeed showed all of the possible stories from our friends, So Facebook created an algorithm to predict how interesting each story will be to each user.Facebook calls as “EdgeRank” because it ranks the edges. It filters each user’s newsfeed to only show the most interesting stories for that user.
How does it work ?
EdgeRank has three primary concept:
1. Affinity score: i.e. Facebook calculates affinity score by looking at explicit actions that users take such as the strength of the action, how close the person who took the action was to you and how long ago they took the action.
2. Edge Weight: i.e. Every action that a user takes creates an edge, and each of those edges, except for clicks, creates a potential story. Each category of edges has a different default weight.

3. Time Decay: As a story gets older, it loses points so rank because it’s “old news.” EdgeRank is a running score – not a one-time score. When a user logs into Facebook, their newsfeed is populated with edges that have the highest score at that very moment in time.

EdgeRank is a thing of the past, and it’s been replaced by a machine learning-based algorithm.The most important thing is understanding the evolving algorithm itself. “If you’re not successful in the news feed, you’re not going to be successful on Facebook.”