Learning to Rank in Entity Relationship Graphs

Louiqa Raschid (), Hassan Sayyadi () and Vagelis Hristidis ()
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Louiqa Raschid: Smith School of Business, University of Maryland, College Park, Maryland 20742
Hassan Sayyadi: Department of Computer Science, University of Maryland, College Park, Maryland 20742
Vagelis Hristidis: Department of Computer Science and Engineering, University of California, Riverside, Riverside, California 92521

INFORMS Journal on Computing, 2019, vol. 31, issue 4, 671-688

Abstract: Many real-world data sets are modeled as entity relationship graphs or heterogeneous information networks. In these graphs, nodes represent entities and edges mimic relationships. ObjectRank extends the well-known PageRank authority flow–based ranking method to entity relationship graphs using an authority flow weight vector ( W ). The vector W assigns a different authority flow–based importance (weight) to each edge type based on domain knowledge or personalization. In this paper, our contribution is a framework for Learning to Rank in entity relationship graphs to learn W, in the context of authority flow. We show that the problem is similar to learning a recursive scoring function. We present a two-phase iterative solution and multiple variants of learning. In pointwise learning, we learn W, and hence the scoring function, from the scores of a sample of nodes. In pairwise learning, we learn W from given preferences for pairs of nodes. To demonstrate our contribution in a real setting, we apply our framework to learn the rank, with high accuracy, for a real-world challenge of predicting future citations in a bibliographic archive—that is, the FutureRank score. Our extensive experiments show that with a small amount of training data, and a limited number of iterations, our Learning to Rank approach learns W with high accuracy. Learning works well with pairwise training data in large graphs.

Keywords: Learning to Rank; ObjectRank; authority flow ranking; entity relationship graphs (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:31:y:2019:i:4:p:671-688

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