If this code helps with your work, please cite:

Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang and Mubbasir Kapadia. HID: Hierarchical Multiscale Representation Learning for Information Diffusion. In International Joint Conference on Artificial Intelligence (IJCAI), 2020.

@inproceedings{zhou2020hid,
    title = {{HID: Hierarchical Multiscale Representation Learning for Information Diffusion}},
    author = {Zhou, Honglu and Xu, Shuyuan and Fu, Zuohui and de Melo, Gerard and Zhang, Yongfeng and Kapadia, Mubbasir},
    booktitle = {IJCAI},
    year = 2020
}

Please go to data folder to find the download links of datasets used in the paper.

• hid.py is the framework.
• utils.py has many general-purpose function including "upscaling" and "downscaling" (both are called by hid.py).
• metrics.py has function implementation for various metrics.
• test.py is the code to run testing and get the testing performance, by calling "test" function from specific model (e.g. CDK).

Run 'conda env create -f environment.yml' to create a conda environment that satisfies the package requirement. Check or modify the conda environment name in the first line of 'environment.yml'.

Hyper-parameters:

• s: num_scales
• p: coarse_portion
• upscaling operator: upoperator

time python hid.py --corpus_path=./data/digg_500user/ --output_path=./data/digg_CDK_s0 --num_scales=0 --max_epochs=8000 --diffuser=CDK

See training log.
See testing log.

time python hid.py --corpus_path=./data/digg_500user/ --output_path=./data/digg_CDK_HAC_s2_p1dot2 --num_scales=2 --coarse_portion=1.2 --max_epochs=2666 --diffuser=CDK --upoperator=HAC

See training log.
See testing log.

time python test.py