Leaderboard Submission Form for TGB-Seq
This is the form for submitting to TGB-Seq with your method results. If you have any questions, please reach out to yilu@ruc.edu.cn.
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Contact Email *
Please provide your email to contact about your submission.
Primary Contact Name
 *
Please provide your own name and a short affiliation name in parentheses, e.g., Lu Yi (RUC).
Name of Your Method
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Please provide the name of your method, e.g., "TGN", "CAWN", "EdgeBank" (maximum character count is 30).
External data
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When building your model, did you use external data (in the form of external pre-trained models, raw text, external unlabeled/labeled data)? If "Yes", please clearly indicate that in your method name above, e.g., TGN (pre-trained on GPT4).
Dataset
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Please provide the name of the dataset (e.g., "Flickr") that you would like the report the performance.
Test Performance
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For the chosen dataset, please report the test performance results output by the TGB-Seq evaluator. Conduct the experiments at least three times to calculate the average and standard deviation. When computing the standard deviation, set the Delta Degrees of Freedom to 1 (in numpy, use np.std(your_mrr_list, ddof=1)). Please format your results as follows: "0.366119, 0.013742", where 0.366119 is the average, and 0.013742 is the standard deviation.

Code Access *

Please provide a link to a public GitHub repository containing all code necessary to reproduce your submitted results. The repository should include a README file with clear instructions on the commands required to run your method. Ensure the link is valid and accessible; placeholder links are not permitted.

Paper Link
 *
Please provide the link to the original paper that describes the method. If your method has any original component (e.g., even just combining existing methods XXX and YYY), you have to write a technical report describing it (e.g., how you exactly combined XXX and YYY).
Tuned Hyper-parameters
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Please kindly disclose all the hyper-parameters you tuned, and how much you tuned for each of them. Please follow the following form: "lr: [0.001*, 0.01], num_layers: [4*,5], hidden_channels: [128, 256*], dropout: [0*, 0.5], max_num_epochs: 50, patience: 5", where the asterisks denote the hyper-parameters you eventually selected (based on validation performance) to report the test performance. This information will not appear in the leaderboard for the time being, but it is important for us to keep the record and encourage the fair model selection.
Implementation
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Is the implementation official (implementation by authors who proposed the method) or unofficial (re-implementation of the method by non-authors)?
The number of parameters of your model
 *
Hardware
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The hardware accelerate (GPU, TPU, etc.) used for the experiments, e.g., GeForce RTX 2080 (11GB GPU). If multiple accelerators  (e.g. multiple GPUs) are used, please specify so.
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