Realtime Top-k Personalized PageRank over Large Graphs on GPUs
Summary: Realtime top-k Personalized PageRank on Internet-scale graphs with GPUs. kPAR combines adaptive forward push and inverted random walks with GPU-aware load balancing, delivering quality guarantees and ~10x CPU speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jieming Shi (National University of Singapore)
- 2. Renchi Yang (Nanyang Technological University)
- 3. Tianyuan Jin (University of Science and Technology Beijing)
- 4. Xiaokui Xiao (National University of Singapore)
- 5. Yin Yang (Hamad Bin Khalifa University)
BibTeX Citation
@article{shi_vldb20,
title = {{Realtime Top-k Personalized PageRank over Large Graphs on GPUs}},
author = {Shi, Jieming and Yang, Renchi and Jin, Tianyuan and Xiao, Xiaokui and Yang, Yin},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {1},
pages = {15--28},
doi = {10.14778/3357377.3357379},
url = {https://doi.org/10.14778/3357377.3357379},
year = {2020}
}
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