Hyperparametric Robust and Dynamic Influence Maximization
Abstract
We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with theoretical quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods.
Cite
Text
Saha et al. "Hyperparametric Robust and Dynamic Influence Maximization." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I12.33362Markdown
[Saha et al. "Hyperparametric Robust and Dynamic Influence Maximization." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/saha2025aaai-hyperparametric/) doi:10.1609/AAAI.V39I12.33362BibTeX
@inproceedings{saha2025aaai-hyperparametric,
title = {{Hyperparametric Robust and Dynamic Influence Maximization}},
author = {Saha, Arkaprava and Cautis, Bogdan and Xiao, Xiaokui and Lakshmanan, Laks V. S.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {12497-12505},
doi = {10.1609/AAAI.V39I12.33362},
url = {https://mlanthology.org/aaai/2025/saha2025aaai-hyperparametric/}
}