SlowGuess/NeurIPS_2023_OCR huggingface dataset:
<sup>3</sup>https://snap.stanford.edu/data/soc-sign-bitcoin-otc.html
4 https://snap.stanford.edu/data/soc-sign-bitcoin-alpha.html
<sup>5</sup>http://snap.stanford.edu/jodie/wikipedia.csv
- TGN is a generic and efficient framework for deep learning on dynamic graphs for discrete representation. We set the number of runs to 10 in our experiments. The max number of sampling neighbors is set to 10 and two heads are used in the attention layer. The dropout probability is 0.1 and the learning rate is 0.0001.
- CAW utilizes a new anonymization strategy to represent a temporal network inductively. We set the dimension of the positional embedding to 108, batch size to 64, and bias to 1e-5. The maximum number of neighbors when sampling is 64.
- TDLG constructs line graphs to model edges directly instead of computing from node embedding. We discard the attributes of the Wikipedia dataset because the original module could not process data with attributes.
- NeurTWs improves the causal anonymous walks strategy in CAW and considers structural and tree traversal properties in the process of walking. The dimension of position embedding is 108 and 32 neighbors are sampled for each node. The temporal bias, spatial bias, and ee bias are set to 1e-5, 1, and 0 respectively.