EdgeRank is the name commonly given to the algorithm Facebook used in the early years of its News Feed to decide which stories a user saw and in what order. Every action a friend or page took - a post, a photo, a comment, a like - was treated as an “edge,” and EdgeRank scored each edge for a given viewer using three broad factors: affinity (how close the relationship is between the viewer and the source, inferred from past interactions), weight (how significant the type of action is, with a comment counting more than a like), and time decay (newer edges rank higher than older ones). Facebook discussed these factors publicly around its 2010 f8 developer conference.
EdgeRank mattered enormously to anyone trying to reach an audience on Facebook, because a page’s posts only appeared in followers’ feeds if they scored highly enough. This created an entire cottage industry of advice on how to “beat” the algorithm, and it made organic reach for brands a moving target that Facebook controlled.
The simple three-factor model did not last. Facebook moved to machine-learning ranking that weighs a large number of signals rather than a fixed formula. By the time of Meta’s 2023 research releases on the 2020 US election, the company described the feed as ordered by a curation algorithm that the studies experimentally replaced with a simple reverse-chronological feed to measure its effects.
Why business readers should care: EdgeRank is the clearest early illustration of a durable fact about platform economics - the owner of the feed controls who reaches whom, and can change the rules at any time. Businesses that built audiences on organic reach learned this when the formula shifted.