Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank
Summary: NRP converts personalized PageRank into scalable homogeneous embeddings via iterative degree-based reweighting, correcting PPR’s source-centric bias. It achieves O(m log n) time and O(m) space, outperforming 18 methods while scaling to billion-edge graphs on one CPU. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Renchi Yang (Nanyang Technological University)
- 2. Jieming Shi (National University of Singapore)
- 3. Xiaokui Xiao (National University of Singapore)
- 4. Yin Yang (Hamad Bin Khalifa University)
- 5. Sourav S. Bhowmick (Nanyang Technological University)
BibTeX Citation
@article{yang_vldb20,
title = {{Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank}},
author = {Yang, Renchi and Shi, Jieming and Xiao, Xiaokui and Yang, Yin and Bhowmick, Sourav S.},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {5},
pages = {670--683},
doi = {10.14778/3377369.3377376},
url = {https://doi.org/10.14778/3377369.3377376},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 16 of 16 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,225 | HubPPR: Effective Indexing for Approximate Personalized PageRank | 2017 | VLDB | 8.9183159e-05 |
| 4,024 | TopPPR: Top-k Personalized PageRank Queries with Precision Guarantees on Large Graphs | 2018 | SIGMOD | 6.9479974e-05 |
| 4,225 | Realtime Top-k Personalized PageRank over Large Graphs on GPUs | 2020 | VLDB | 6.821373e-05 |
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