• Python (>=3.8)
• PyTorch (>=1.9.0)
• NumPy (>=1.22.3)
• Pandas (>=1.4.1)
• Scikit-Learn (>=1.0.2)
• Scipy (>=1.9.0)
• Networkx (>=2.6.3)
• DGL (>=0.8.1)

DBLP [Michael Ley. 2009. DBLP - Some Lessons Learned.Proc. VLDB Endow.2, 2 (2009),1493–1500. ]

sx-mathoverflow: http://snap.stanford.edu/data/sx-mathoverflow.html

CollegeMsg: http://snap.stanford.edu/data/CollegeMsg.html

ia-retweet-pol: https://networkrepository.com/ia-retweet-pol.php

Facebook: https://networkrepository.com/fb-messages.php

Email: http://snap.stanford.edu/data/email-Eu-core-temporal.html

Here we provide the implementation of TF-GCL.

• To train and evaluate, just run:

python run_retweet.py

or

python main_dynamicCL_ver1.py --h_feats_list 256 --data=ReTweet --epochs=2000 --emb_size=256 --lr=5e-3 --cl_loss_weight=1.0 --cl_batch_size=256 --neg_class_weight=0.4 --temporal_weight_loss=true --cl_loss=true --decay_by=day --ts_decay_rate=0.01 --concat=false --temporal_cl_loss=true --neighbor_as_pos=true --symmetric_cl_loss=true --eval_epochs=1 --fea_drop_percent=0.6 --edge_pert_percent=0.6 --ts_mask_percent=0.6

h_feats_list construct the GCNs encoder, for example --h_feats_list 256 128 construct a two layer GCNs encoder, which have 256 and 128 hidden dimension.

cl_batch_size is the balance weight of TF-GCL

fea_drop_percent is the drop rate of augmentation feature masking

edge_pert_percent is the edge ratio of augmentation edge perturbation

ts_mask_percent is the edge ratio of augmentation timestamp masking

G.edges

where G.edges is the edgelist file with timestamps. column 1 is the start node, column 2 is the destination node, and column 3 is the timestamp of the edge.

3 9 0
1 3 13
2 4 50
...

In dataload directory, we preprocessing the G.edges file, and split it to train.edges and test.edges for temporal link prediction evaluation.