Amy Hodler gives us a quick summary of fifteen separate algorithms for traversing a graph in Neo4j:
6. PageRank
What it does: Estimates a current node’s importance from its linked neighbors and then again from their neighbors. A node’s rank is derived from the number and quality of its transitive links to estimate influence. Although popularized by Google, it’s widely recognized as a way of detecting influential nodes in any network.
How it’s used: PageRank is used in quite a few ways to estimate importance and influence. It’s used to suggest Twitter accounts to follow and for general sentiment analysis.
PageRank is also used in machine learning to identify the most influential features for extraction. In biology, it’s been used to identify which species extinctions within a food web would lead to biggest chain reaction of species death.
If you are interested in getting into graph databases, it’s useful to know these algorithms.