This repository reproduces the results in the paper Understanding Influence Maximization via Higher-Order Decomposition (Zonghan Zhang et al., SIAM Data Mining 2023), whose objective is to learn the higher-order relationship in selecting seeds in information maximization problem.
For empirical experiments in the paper, please refer to the files below:
python case-study.pyOur result shows that the proposed lightweighted plugin can improve SOTA model (red) by a significant margin (blue)
Inside our experiments, IC (independen cascade) and LT (linear threshold) will be evaluated on each method. Details for IC and LT can be found in
SIM-IC.py
SIM-LT.py
If you compare with, build on, or use aspects of the SIM, please cite the following:
@inproceedings{zhang2023sim,
title={Understanding Influence Maximization via Higher-Order Decomposition},
author={Zhang, Zonghan, and Zhiqian Chen},
booktitle={Proceedings of SIAM International Conference on Data Mining (SDM)},
year={2023},
}