Reddit - http://snap.stanford.edu/jodie/reddit.csv
Wikipedia - http://snap.stanford.edu/jodie/wikipedia.csv
MOOC - http://snap.stanford.edu/jodie/mooc.csv
LastFM - http://snap.stanford.edu/jodie/lastfm.csv
Preprocessed Enron and UCI datasets are taken from - https://github.com/snap-stanford/CAW
If one wants to use raw datasets use the preprocessing code given in preprocess_data.py and use it as,
python preprocess_data.py --data_dir "your raw data dir" --data "name""
usage: Interface for data preprocessing [-h] [--data_dir DATA_DIR] [--data DATA] [--bipartite]
[--num_node_feats NUM_NODE_FEATS]
optional arguments:
-h, --help show this help message and exit
--data_dir DATA_DIR Dataset Directory
--data DATA Dataset name (eg. reddit, wikipedia, mooc, lastfm)
--bipartite Whether the graph is bipartite
--num_node_feats NUM_NODE_FEATS
Number of random node features
For all parameters of training TGRank follow the commands
python tgrank_listwise_train.py -h
usage: TGRank Interaction Ranking Listwise Training [-h] --data_dir DATA_DIR [--data DATA] [--prefix PREFIX] [--train_batch_size TRAIN_BATCH_SIZE]
[--eval_batch_size EVAL_BATCH_SIZE] [--num_epochs NUM_EPOCHS] [--num_layers NUM_LAYERS] [--lr LR]
[--emb_dim EMB_DIM] [--time_dim TIME_DIM] [--num_temporal_hops NUM_TEMPORAL_HOPS] [--num_neighbors NUM_NEIGHBORS]
[--uniform_sampling] [--patience PATIENCE] [--log_dir LOG_DIR] [--saved_models_dir SAVED_MODELS_DIR]
[--saved_checkpoints_dir SAVED_CHECKPOINTS_DIR] [--verbose VERBOSE] [--seed SEED]
[--num_temporal_hops_eval NUM_TEMPORAL_HOPS_EVAL] [--num_neighbors_eval NUM_NEIGHBORS_EVAL]
[--no_fourier_time_encoding] [--coalesce_edges_and_time] [--train_randomize_timestamps] [--no_id_label]
optional arguments:
-h, --help show this help message and exit
--data_dir DATA_DIR Dataset directory
--data DATA Dataset name (eg. reddit, wikipedia, mooc, lastfm, enron, uci)
--prefix PREFIX Prefix to name the checkpoints and models
--train_batch_size TRAIN_BATCH_SIZE
Train batch size
--eval_batch_size EVAL_BATCH_SIZE
Evaluation batch size (should experiment to make it as big as possible (based on available GPU memory))
--num_epochs NUM_EPOCHS
Number of training epochs
--num_layers NUM_LAYERS
Number of layers
--lr LR
--emb_dim EMB_DIM Embedding dimension size
--time_dim TIME_DIM Time Embedding dimension size. Give 0 if no time encoding is not to be used
--num_temporal_hops NUM_TEMPORAL_HOPS
No. of temporal hops for sampling candidates during training.
--num_neighbors NUM_NEIGHBORS
No. of neighbors to sample for each candidate node at each temporal hop. This is also the same parameter that samples edges.
--uniform_sampling Whether to use uniform sampling for temporal neighbors. Default is most recent sampling.
--patience PATIENCE Patience for early stopping
--log_dir LOG_DIR directory for storing logs.
--saved_models_dir SAVED_MODELS_DIR
directory for saved models.
--saved_checkpoints_dir SAVED_CHECKPOINTS_DIR
directory for saved checkpoints.
--verbose VERBOSE Verbosity 0/1 for tqdm
--seed SEED deterministic seed for training. this is different from that by used neighbor finder which uses a local random state
--num_temporal_hops_eval NUM_TEMPORAL_HOPS_EVAL
No. of temporal hops for sampling candidates during evaluation.
--num_neighbors_eval NUM_NEIGHBORS_EVAL
No. of neighbors to sample for each candidate node at each temporal hop during evaluation. This is also the same parameter that samples edges.
--no_fourier_time_encoding
Whether to not use fourier time encoding
--coalesce_edges_and_time
Whether to coalesce edges and time. make sure no_fourier_time_encoding is set and time_dim is 1. else will raise error
--train_randomize_timestamps
Whether to randomize train timestamps i.e. after sampling and before going into TSAR
--no_id_label Whether to not use identity label to distinguish source from destinations. Value used to set label diffusion
All default parameters are given in the help command.
python tgrank_listwise_train.py --data_dir "your data dir" --data enron --prefix tgrank-listwise --verbose 1
python tgrank_listwise_train.py --data_dir "your data dir" --data wikipedia --prefix tgrank-listwise --verbose 1
python tgrank_listwise_train.py --data_dir "your data dir" --data mooc --prefix tgrank-listwise --verbose 1
Here running wikipedia without source specific label diffusion
python tgrank_listwise_train.py --data_dir "your data dir" --data wikipedia --prefix tgrank-listwise --verbose 1 --no_id_label
Similarly, one can use different parameters like:
--coalesce_edges_and_time,
--train_randomize_timestamps,
--no_fourier_time_encoding