Hi All, I am wondering if there is an algorithm that can identify nodes that are similar based upon there relationships to other nodes of a different type. For example, if I have a graph of people and items purchased, I would like to be able to identify people with similar buying habits. I think this is different from cliques in that the similar nodes may not know each other. A popular application would be how Netflix can retrieve a list of users with similar tastes to your own, based upon your movie ratings (a different type of node), as opposed to how Facebook suggest friends base upon mutual friends (similar types of nodes).
I suppose there are at least two main approaches to the solution; one where no preprocessing to the data is performed but instead a query returns the most similar node by traversing the graph starting from the input node; and another where somehow an algorithm maps these nodes into some (2-dimensional?) space similar to how layout algorithms work, and then the query would for similar nodes would be a spatial query where the distance to the input node equates to degree of similarity. If seems to me like this concept would be prevalent in document management. Am I on the right track? Are there algorithms or other research for this out there? Any suggestions on how to structure a graph to support this type of query? Thanks, Paul Jackson _______________________________________________ Neo4j mailing list [email protected] https://lists.neo4j.org/mailman/listinfo/user
