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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 stopped using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account.
Art, Impact & Overview
Explore the main themes, entities and connections around EdgeRank. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
facebook algorithm uses content reach factors organic news feed impact see user shown like reaction haha comments ranking pages name
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| EdgeRank | is a | name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed | 0.90 | text |
| EdgeRank | has impact | As | 0.60 | section |
| EdgeRank | has impact | 0.60 | section | |
| EdgeRank | related to External links | How News Feed Works | 0.60 | section |
| EdgeRank | related to Formula and factors | In | 0.60 | section |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.