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Single-Source Personalized PageRanks With Workload Robustness
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AI
DOI：10.1109/TKDE.2022.3175814.png)
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摘要
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Given a source node $s$s and a target node $t$t in a graph $G$G, the Personalized PageRank (PPR) from $s$s to $t$t is the probability of a random walk starting from $s$s terminates at $t$t. PPR is a classic measure of the relevance between two nodes in a graph. It has been applied in numerous real-world systems. However, existing techniques for PPR queries are not robust to dynamic real-world graphs, which typically have different evolving speeds. Their performance is significantly degraded either at a lower graph evolving rate (e.g., much more queries than updates) or a higher rate. To address the above deficiencies, we propose Agenda to efficiently process, with strong approximation guarantees, the single-source PPR (SSPPR) queries on dynamically evolving graphs with various evolving speeds. Compared with previous methods, Agenda has significantly better workload robustness, while ensuring the same result accuracy. Agenda also has theoretically-guaranteed small query and update costs. Experiments on up to billion-edge scale graphs show that Agenda significantly outperforms state-of-the-art methods for various query/update workloads, while maintaining better or comparable approximation accuracies.
Keyword:
Social networking (online)
Indexes
Blogs
Costs
Robustness
Heuristic algorithms
Query processing
Graphs algorithms
personalized pageranks
query processing
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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