Issue No. 04 - April (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.52
Vagelis Hristidis , Dept. of Comput. Sci. & Eng., Univ. of California, Riverside, Riverside, CA, USA
Yao Wu , Microsoft, Redmond, WA, USA
Louiqa Raschid , Robert H. Smith Sch. of Bus., Univ. of Maryland, College Park, MD, USA
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: Node-based personalization, where authority originates from a set of user-specific nodes; edge-based personalization, where the importance of different edge types is user-specific. We propose the first approach to achieve efficient edge-based personalization using a combination of precomputation and runtime algorithms. In particular, we apply our method to ObjectRank, where a personalized 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. In contrast, DataApprox has a provable error bound. Both SchemaApprox and DataApprox are expensive so we develop efficient heuristic implementations, ScaleRank and PickOne respectively. Extensive experiments on the DBLP data graph show that ScaleRank provides a fast and accurate personalized authority flow ranking.
Vectors, Correlation, Approximation algorithms, Least squares approximations, Euclidean distance,approximation algorithms, Object search, personalization, PageRank
Vagelis Hristidis, Yao Wu, Louiqa Raschid, "Efficient Ranking on Entity Graphs with Personalized Relationships", IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. , pp. 850-863, April 2014, doi:10.1109/TKDE.2013.52