Scalable Link-based Personalization for Ranking in Entity-Relationship Graphs

Authority flow techniques like PageRank and ObjectRank can provide personalized ranking of typed entity-relationship graphs. There are two main ways to personalize authority flow ranking: Nodebased personalization, where authority originates from a set of userspecific nodes; Edge-based personalization, where the importance of different edge types is user-specific. We propose for the first time an approach to achieve efficient edge-based personization using a combination of precomputation and runtime algorithms. In particular, we apply our method to the personalized authority flow bounds of ObjectRank, i.e., a weight assignment vector (WAV) assigns different weights to each edge type or relationship type. Our approach includes a repository of rankings for various WAVs. We consider the following two classes of approximation: (a) SchemaApprox is formulated as a distance minimization problem at the schema level; (b) DataApprox is a distance minimization problem at the data graph level. SchemaApprox is not robust since it does not distinguish between important and trivial edge types based on the edge distribution in the data graph. Both SchemaApprox and DataApprox are expensive so we develop efficient heuristic implementations. ScaleRank is an efficient linear programming solution to DataApprox. PickOne is a greedy heuristic for SchemaApprox. Extensive experiments on the DBLP data graph show that ScaleRank provides a fast and accurate personalized authority flow ranking.