Opinion Optimization in Directed Social Networks
Abstract
Shifting social opinions has far-reaching implications in various aspects, such as public health campaigns, product marketing, and political candidates. In this paper, we study a problem of opinion optimization based on the popular Friedkin-Johnsen (FJ) model for opinion dynamics in an unweighted directed social network with n nodes and m edges. In the FJ model, the internal opinion of every node lies in the closed interval [0, 1], with 0 and 1 being polar opposites of opinions about a certain issue. Concretely, we focus on the problem of selecting a small number of k
Cite
Text
Sun and Zhang. "Opinion Optimization in Directed Social Networks." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I4.25585Markdown
[Sun and Zhang. "Opinion Optimization in Directed Social Networks." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/sun2023aaai-opinion/) doi:10.1609/AAAI.V37I4.25585BibTeX
@inproceedings{sun2023aaai-opinion,
title = {{Opinion Optimization in Directed Social Networks}},
author = {Sun, Haoxin and Zhang, Zhongzhi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {4623-4632},
doi = {10.1609/AAAI.V37I4.25585},
url = {https://mlanthology.org/aaai/2023/sun2023aaai-opinion/}
}