Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81350
Title: HubPPR: Effective Indexing for Approximate Personalized PageRank
Authors: Wang, Sibo
Tang, Youze
Xiao, Xiaokui
Yang, Yin
Li, Zengxiang
Keywords: Personalized PageRank
Indexing scheme
Issue Date: 2016
Source: Wang, S., Tang, Y., Xiao, X., Yang, Y., & Li, Z. (2016). HubPPR: Effective Indexing for Approximate Personalized PageRank. Proceedings of the VLDB Endowment, 10(3), 205-216.
Series/Report no.: Proceedings of the VLDB Endowment
Abstract: Personalized PageRank (PPR) computation is a fundamental operation in web search, social networks, and graph analysis. Given a graph G, a source s, and a target t, the PPR query Π(s, t) returns the probability that a random walk on G starting from s terminates at t. Unlike global PageRank which can be effectively pre-computed and materialized, the PPR result depends on both the source and the target, rendering results materialization infeasible for large graphs. Existing indexing techniques have rather limited effectiveness; in fact, the current state-of-the-art solution, BiPPR, answers individual PPR queries without pre-computation or indexing, and yet it outperforms all previous index-based solutions. Motivated by this, we propose HubPPR, an effective indexing scheme for PPR computation with controllable tradeoffs for accuracy, query time, and memory consumption. The main idea is to pre-compute and index auxiliary information for selected hub nodes that are often involved in PPR processing. Going one step further, we extend HubPPR to answer top-k PPR queries, which returns the k nodes with the highest PPR values with respect to a source s, among a given set T of target nodes. Extensive experiments demonstrate that compared to the current best solution BiPPR, HubPPR achieves up to 10x and 220x speedup for PPR and top-k PPR processing, respectively, with moderate memory consumption. Notably, with a single commodity server, HubPPR answers a top-k PPR query in seconds on graphs with billions of edges, with high accuracy and strong result quality guarantees.
URI: https://hdl.handle.net/10356/81350
http://hdl.handle.net/10220/43455
ISSN: 2150-8097
DOI: 10.14778/3021924.3021936
Rights: This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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