Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently
Abstract
Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination strategies, relies heavily on sampling from a model. This is either inefficient or inaccurate. As alternative, we present an efficient message passing algorithm that computes the probability distribution of the cascade size for the Independent Cascade Model on weighted directed networks and generalizations. Our approach is exact on trees but can be applied to any network topology. It approximates locally treelike networks well, scales to large networks, and can lead to surprisingly good performance on more dense networks, as we also exemplify on real world data.
Cite
Text
Burkholz and Quackenbush. "Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I8.16844Markdown
[Burkholz and Quackenbush. "Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/burkholz2021aaai-cascade/) doi:10.1609/AAAI.V35I8.16844BibTeX
@inproceedings{burkholz2021aaai-cascade,
title = {{Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently}},
author = {Burkholz, Rebekka and Quackenbush, John},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {6840-6849},
doi = {10.1609/AAAI.V35I8.16844},
url = {https://mlanthology.org/aaai/2021/burkholz2021aaai-cascade/}
}