Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Local Graph Partitioning using PageRank Vectors
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Dynamic personalized pagerank in entity-relation graphs
Proceedings of the 16th international conference on World Wide Web
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Fast algorithms for topk personalized pagerank queries
Proceedings of the 17th international conference on World Wide Web
Index Design for Dynamic Personalized PageRank
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Fast nearest-neighbor search in disk-resident graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast incremental and personalized PageRank
Proceedings of the VLDB Endowment
Fast personalized PageRank on MapReduce
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Index design and query processing for graph conductance search
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient personalized pagerank with accuracy assurance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the metric of hub-length to partition and schedule random walk tours so that the approximation error reduces exponentially over iterations. Furthermore, as tours are segmented by hubs, the shared substructures between different tours (around the same hub) can be reused to speed up query processing both within and across iterations. Finally, we evaluate FastPPV over two real-world graphs, and show that it not only significantly outperforms two state-of-the-art baselines in both online and offline phrases, but also scale well on larger graphs. In particular, we are able to achieve near-constant time online query processing irrespective of graph size.