pandas==0.24.2
tqdm==4.41.1
numpy==1.16.4
scikit_learn==0.22.1
matploblib==3.3.1
numba==0.51.2

cd processed/
unzip data.zip

u, i, ts, label, idx

  -h, --help            show this help message and exit
  -d {wikipedia,reddit,socialevolve,uci,enron,socialevolve_1month,socialevolve_2weeks}, --data {wikipedia,reddit,socialevolve,uci,enron,socialevolve_1month,socialevolve_2weeks}
                        data sources to use, try wikipedia or reddit
  -m {t,i}, --mode {t,i}
                        transductive (t) or inductive (i)
  --n_degree [N_DEGREE [N_DEGREE ...]]
                        a list of neighbor sampling numbers for different hops, when only a single element is input n_layer
                        will be activated
  --n_layer N_LAYER     number of network layers
  --bias BIAS           the hyperparameter alpha controlling sampling preference with time closeness, default to 0 which is
                        uniform sampling
  --agg {tree,walk}     tree based hierarchical aggregation or walk-based flat lstm aggregation
  --pos_enc {spd,lp,saw}
                        way to encode distances, shortest-path distance or landing probabilities, or self-based anonymous
                        walk (baseline)
  --pos_dim POS_DIM     dimension of the positional embedding
  --pos_sample {multinomial,binary}
                        two practically different sampling methods that are equivalent in theory
  --walk_pool {attn,sum}
                        how to pool the encoded walks, using attention or simple sum, if sum will overwrite all the other
                        walk_ arguments
  --walk_n_head WALK_N_HEAD
                        number of heads to use for walk attention
  --walk_mutual         whether to do mutual query for source and target node random walks
  --walk_linear_out     whether to linearly project each node's
  --attn_agg_method {attn,lstm,mean}
                        local aggregation method, we only use the default here
  --attn_mode {prod,map}
                        use dot product attention or mapping based, we only use the default here
  --attn_n_head ATTN_N_HEAD
                        number of heads used in tree-shaped attention layer, we only use the default here
  --time {time,pos,empty}
                        how to use time information, we only use the default here
  --n_epoch N_EPOCH     number of epochs
  --bs BS               batch_size
  --lr LR               learning rate
  --drop_out DROP_OUT   dropout probability for all dropout layers
  --tolerance TOLERANCE
                        toleratd margainal improvement for early stopper
  --seed SEED           random seed for all randomized algorithms
  --ngh_cache           (currently not suggested due to overwhelming memory consumption) cache temporal neighbors previously
                        calculated to speed up repeated lookup
  --gpu GPU             which gpu to use
  --cpu_cores CPU_CORES
                        number of cpu_cores used for position encoding
  --verbosity VERBOSITY
                        verbosity of the program output

@inproceedings{
wang2021inductive,
title={Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks},
author={Yanbang Wang and Yen-Yu Chang and Yunyu Liu and Jure Leskovec and Pan Li},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=KYPz4YsCPj}
}