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EvePPR: Everything Evolves in Personalized PageRank

This repository is for the WWW' 2023 paper "Everything Evolves in Personalized PageRank" (Link). EvePPR and EvePPR-APP (approximated EvePPR) can efficiently and accurately track the Personalized PageRank vector after the transition matrix or stochastic vector has changed.

Have a try

cd code
python main.py

Full code for our experiment

In our research paper, we run three temporal graph alignment scenarios on movielens-1m, bitcoinalpha and wikilens datasets. The code can be downloaded from Google Drive Link or this repository. The full code contains datasets and saved intermediate data (so that the user can simply np.load/sp.load instead of taking a long time recalculating everything). If you're not researching on temporal graph alignment topic, the simplified version in this repo should suffice.

The dataset we used in our experiments are processed movielens-1m, bitcoinalpha and wikilens.

Code for the baselines within our scenarios can be found in the repositories of this github account: Violet24K.

Reference

@inproceedings{DBLP:conf/www/LiFH23,
  author       = {Zihao Li and
                  Dongqi Fu and
                  Jingrui He},
  title        = {Everything Evolves in Personalized PageRank},
  booktitle    = {Proceedings of the {ACM} Web Conference 2023, {WWW} 2023, Austin,
                  TX, USA, 30 April 2023 - 4 May 2023},
  pages        = {3342--3352},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3543507.3583474},
  doi          = {10.1145/3543507.3583474},
  timestamp    = {Tue, 02 May 2023 14:07:23 +0200},
  biburl       = {https://dblp.org/rec/conf/www/LiFH23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}