Datasets

Plain Graphs

Welcome to cite our paper if you publish results based on our preprocessed datasets.

@article{yang13homogeneous,
  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={Proceedings of the VLDB Endowment},
  volume={13},
  number={5}
}

@article{shi13realtime,
  title={Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs},
  author={Shi, Jieming and Jin, Tianyuan and Yang, Renchi and Xiao, Xiaokui and Yang, Yin},
  journal={Proceedings of the VLDB Endowment},
  volume={13},
  number={7}
}

Attributed Graphs

Tips: node attributes in our preprocessed datasets are compressed as “attrs.pkl” file via cPickle package in Python 2.7 or “attrs.npz” file, which can be loaded as a sparse attribute matrix by using the following code

import cPickle as pickle
features = pickle.load(open("attrs.pkl"))

or

from scipy import sparse
features = sparse.load_npz("attrs.npz")

Welcome to cite our paper if you publish results based on our preprocessed datasets.

@article{yang2020scaling,
  title={Scaling Attributed Network Embedding to Massive Graphs},
  author={Yang, Renchi and Shi, Jieming and Xiao, Xiaokui and Yang, Yin and Liu, Juncheng and Bhowmick, Sourav S},
  journal={Proceedings of the VLDB Endowment},
  volume={14},
  number={1},
  pages={37--49},
  year={2021},
  publisher={VLDB Endowment}
}