HID: Hierarchical Multiscale Representation Learning for Information Diffusion

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
}

Dataset

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

Code

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'.

How to run

Hyper-parameters:

s: num_scales
p: coarse_portion
upscaling operator: upoperator

How to run HID without using upscaling and downscaling (just run baseline) for training and learning:

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.

How to run HID with upscaling and downscaling for training and learning (e.g. s=2, p=1.2):

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