wanghuimu / ldg

PyTorch code for "Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions"

LDG

PyTorch code for our paper on Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions.

Data

All data are uploaded to this repo. The original data can be accessed here.

Before running the code, unpack Proximity.csv.bz2, e.g. by running bzip2 -d Proximity.csv.bz2 inside the SocialEvolution folder.

Examples

Running the baseline DyRep model [1]:

python main.py --log_interval 300 --epochs 5 --data_dir ./SocialEvolution/.

Running our latent dynamic graph (LDG) model with a learned graph, sparse prior and biliear interactions:

python main.py --log_interval 300 --epochs 5 --data_dir ./SocialEvolution/ --encoder mlp --bilinear --sparse

Note that our default option is to filter Proximity events by their probability: --prob 0.8. In the DyRep paper, they use all events, i.e. --prob 0.8. When we compare results in our paper, we use the same --prob 0.8 for all methods.

If you make use of this code, we appreciate it if you can cite our paper as follows:

@ARTICLE{Knyazev2019-zj,
  title         = "Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions",
  author        = "Knyazev, Boris and Augusta, Carolyn and Taylor, Graham W",
  month         =  sep,
  year          =  2019,
  archivePrefix = "arXiv",
  primaryClass  = "stat.ML",
  eprint        = "1909.10367"
}

[1] Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. DyRep: Learning representations over dynamic graphs. In ICLR, 2019