GDELT-ComplexEvent

This is the code and data for the article "SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting".

The repo includes:

1. An automated data construction pipeline for the Structured, Complex, and Time-complete Temporal Events (SCTc-TE).
2. Two large-scale complex event datasets, named MidEast-TE and GDELT-TE.
3. A proposed model LoGo that leverages both local and global contexts for the newly formulated task: SCTc-TE forecasting.

Environment

The code is tested to be runnable under the environment with python=3.9; pytorch=1.12; cuda=11.3.

To create a new environment, you could use the commands below:

conda create --name logo python=3.9
conda activate logo
conda install pytorch==1.12.0 -c pytorch
pip install pandas
pip install tensorboard
conda install tqdm
conda install -c dglteam dgl-cuda11.3

Dataset

The input data files need to be unzipped first:

unzip data/MIDEAST_CE.zip -d data
unzip data/GDELT_CE.zip -d data

The data folder then contains:

• CAEMO: Information from CAMEO ontology, organized in python dictionaries.
• MIDEAST_CE: SCTc-TE extracted by the Vicuna model.
• GDELT_CE: SCTc-TE constructed from GDELT data.

Dataset Construction Pipeline

To construct the MIDEAST_CE and GDELT_CE data from raw data, the pipeline is collected and described in ./dataset_construction.

Data Preparation

Before training the model, you need to generate both the local and global graphs:

python generate_graphs_ce.py --dataset MIDEAST_CE
python generate_graphs_ce.py --dataset GDELT_CE

Experiment

The searched training hyperparameter configuration is stored in config.yaml.

Hyperparameters can also be set in command, see detailed usage in train_logo_early.py:get_cmd().

[dataset_name] is MIDEAST_CE or GDELT_CE

Train LoGo model:

python train_logo_early.py -d [dataset_name] --m LoGo_sep

Train LoGo ablations:

• LoGo_local:

python train_logo_late.py -d [dataset_name] --local_only

• LoGo_global:

python train_logo_late.py -d [dataset_name] --global_only

• LoGo_share:

python train_logo_early.py -d [dataset_name] --m LoGo_share

• LoGo_late:

python train_logo_late.py -d [dataset_name] --m LoGo_sep

View results:

Loss and runs:

tensorboard --logdir=./runs/

Results in ./logs folder.