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| Field | Value |
| Title | Index design for dynamic personalized PageRank |
| Names |
PATHAK, AMIT
CHAKRABARTI, SOUMEN GUPTA, MANISH |
| Date Issued | 2008 (iso8601) |
| Abstract | Personalized PageRank, related to random walks with restarts and conductance in resistive networks, is a frequent search paradigm for graph-structured databases. While efficient batch algorithms exist for static whole-graph PageRank, interactive query-time personalized PageRank has proved more challenging. Here we describe how to select and build indices for a popular class of PageRank algorithms, so as to provide real-time personalized PageRank and smoothly trade off between index size, preprocessing time, and query speed. We achieve this by developing a precise, yet efficiently estimated performance model for personalized PageRank query execution. We use this model in conjunction with a query workload in a cost-benefit type index optimizer. On millions of queries from CITESEER and its data graphs with 74-320 thousand nodes, our algorithm runs 50-400x faster than whole-graph PageRank, the gap growing with graph size. Index size is 10-20% of a text index. Ranking accuracy is above 94%. |
| Genre | Article |
| Topic | Numerical Analysis |
| Identifier | Proceedings of the IEEE 24th International Conference on Data Engineering, Cancún, Mexico, 7-12 April 2008, 1489-1491 |