Facebook EdgeRank explained

EdgeRank and Graph Rank Demystified Have you ever wondered how Facebook determines what posts will populate your newsfeed? İt's actually based upon an algorithm known as the: Facebook EdgeRank Algorithm Σ Eu.w.d, edges e in which: U : stands for the Affinity Score W : stands for the Weight Score d : stands for the Recency Score Ue The Affinity Score measures the affinity between two users User 1, being the person who created an object (like writing a status update) or took action on an object (like sharing the status update) User 2, being the person who is viewing the post in the "Top News" feed The more engagement history between the two, the higher the affinity score Let's look at Mark and John for example... John REGULARLY comments on Mark's posts This will lead to Facebook believing that the affinity of John towards Mark is high. Therefore, Facebook will show more of Marks's posts on John's "Top News" feed I LIKE PIZZA! I LOVE PIZZA TOO!!! However, the affinity score is one-directional, so let's flip the case Mark RARELY comments on John's posts This will lead to Facebook I LIKE BUBBLE BATHS!! believing that the affinity of Mark towards John is low. Therefore, Facebook will show less of John's posts on Mark's "Top News" feed W. The Weight Score measures the importance of an object you create in terms of a resulting action like a comment, share or a like Different objects have different levels of interactions. For example: Status updates < Photo Updates < Video Updates Objects which receive more number of likes and have been shared multiple times will show up more in the news feed compared to those with low engagement d. The Recency Score is based on the time the object was posted As seen in the graph, Newer objects will have a higher recency score than objects that have been posted earlier Time What is GRAPH RANK? Affinity + Weight + Interactions + Time decay Graph Rank Essentially, Graph Rank is to application owmers and users, what EdgeRank is to Fan Page owners. Graph Rank consists of a set of rules that are part of EdgeRank but are only applied to updates coming from apps. An app is more likely to appear in a fan's newsfeed if it has high scores for all of the variables above. The number of people with common interests that are checked into locations in the same area adds another multiplication that affects which app updates will appear in their newsfeeds. So, proximity and shared interests are two important factors that Graph Rank looks at to determine rankings. Also when Facebook comes out with a new feature, more weight will be given to the newcomer as a way of promotion How can you improve your EdgeRank? Bill has an art gallery coming up, but he hasn't updated his fan page in a while, so before he posts he should improve his EdgeRank to ensure his story gets heard! If he works on each Variable, he can improve the score! For instance he could create a poll that get peoples attention, using Facebook ads and getting the poll sponsored would be a good idea as well! Posting more photos of his work would be a good idea for increasing interaction and weight score! Even though the EdgeRank works only one way, responding to subscribers can lead to more interaction, and helping Bill's fans remember his event! http://www.insidefacebook.com/2011/12/27/edgerank-and-graph-rank-defined/ http://econsultancy.com/us/blog/7885-the-ultimate-guide-to- the-facebook-edgerank-algorithm http://www.indrashishghosh.com/understand-edgerank-get-your- updates-seen-on-facebook/ http://blog.involver.com/2011/10/25/facebook-edgerank-and-graphrank-explained/ http://www.omarkattan.com/2012/02/21/essential-guide-to-facebooks-graph-rank/ http://www.shortstack.com/2011/12/the-difference-between-edgerank-and-graph-rank/ *The Graph Rank fomula is similar to EdgeRank, and is equal to the sumation of the three coefficients. It is not a simple sumation of three fixed integers. Created By: COPYPRESS Recency Score