Overview
TGB-Seq comprises eight temporal graph datasets that exhibit intricate sequential dynamics inherently. It provides both a dataloader and an evaluator for ease of use. The dataloader manages automatic downloading and preprocessing, and the evaluator provides standardized evaluation with fixed negative samples and metrics.
Install
You can install TGB-Seq using Python package manager pip.
pip install tgb-seq
Requirements
- Python>=3.9
- numpy>2.0
- pandas>=2.2.3
- huggingface-hub>=0.26.0
- torch>=2.5.0
Package Usage
Quick Start
Get started with TGB-Seq using this quick-start example built on DyGLib. Just follow the commands below to begin your journey with TGB-Seq! 🚀🚀🚀
pip install tgb-seq
git clone git@github.com:TGB-Seq/TGB-Seq.git
cd examples
python train_link_prediction.py --dataset_name GoogleLocal --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --gpu 0 --batch_size 200 --dropout 0.1 --sample_neighbor_strategy recent
To submit your results to the TGB-Seq leaderboard, please fill in this Google Form.
Dataloader
For example, to load the Flickr dataset to ./data/, run the following code:
from tgb_seq.LinkPred.dataloader import TGBSeqLoader
data=TGBSeqLoader("Flickr", "./data/")
Then, Flickr.csv and Flickr_test_ns.npy will be downloaded from Hugging Face automatically into ./data/Flickr/. The arrays of source nodes, destination nodes, interaction times, negative destination nodes for the test set can be accessed as follows.
src_node_ids=data.src_node_ids
dst_node_ids=data.dst_node_ids
node_interact_times=data.node_interact_times
test_negative_samples=data.negative_samples
If you encounter any network errors when connecting to Hugging Face, you can use the Hugging Face mirror site to download the dataset. To do so, run the following command in your terminal:
export HF_ENDPOINT=https://hf-mirror.com
We also provide all the TGB-Seq datasets on Google Drive and their original datasets here.
Evaluator
Up to now, all the TGB-Seq datasets are evaluated by the MRR metric. The evaluator takes positive_probabilities with size as (batch_size,) and negative_probabilities with size as (batch_size x number_of_negatives) as inputs and outputs the rank of eash positive sample with size as (batch_size).
from tgb_seq.LinkPred.evaluator import Evaluator
evaluator=Evaluator()
# For the MRR metric, this will return a list of rank,
# each represents the rank of one positive sample.
result_dict=evaluator.eval(positive_probabilities,negative_probabilities)
Citing TGB-Seq
If you use TGB-Seq datasets, please cite our paper.