GDGB Leaderboard Submission Form
This is the form for submitting to GDGB Team with your method results. If you have any questions, please reach out to peng_jie@ruc.edu.cn.
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., Jie Peng (RUC).
Name of Your Method
 *
Please provide the name of your method, e.g., "GAG-General".
External data *
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., GAG-General (pre-trained on Llama3). If "No", please input "No".
Dataset
 *
Please provide the name of the dataset (e.g., "Sephora") that you would like to report the performance.
Task *
Please choose the type of the task that you would like to report the performance.
Test Performance
 *
For the selected dataset and task, please report the DyTAG generation performance using our proposed evaluation protocols in GDGB: 1) Graph Structural Metric, 2) Textual Quality Metric, and 3) Graph Embedding Metric. 
Example input: 1) Graph Structural Metric: Degree MMD:0.023, Spectra MMD:0.011, D_k:0.143, alpha:2.993, Power-law Validity:True; 2) Textual Quality Metric:4.37; 3) Graph Embedding Metric:0.758.
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
 *
Please kindly disclose all the hyper-parameters you tuned, and how much you tuned for each of them. Example form: "learning rate: [1e-5*, 1e-4, 1e-3], num_layers: [1, 2, 3*, 4, 5], ...", where the asterisks denote the hyper-parameters you eventually selected 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 comparison.
Implementation
 *
Is the implementation official (implementation by authors who proposed the method) or unofficial (re-implementation of the method by non-authors)?
Model Size
 *
Please input  the number of parameters of your model.
Hardware
 *
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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