Maximizing Influence Spread Through a Dynamic Social Network (Student Abstract)

AAAI 2023 pp. 16316-16317

doi:10.1609/AAAI.V37I13.27018 /aaai/2023/schierreich2023aaai-maximizing/

Abstract

Modern social networks are dynamic in their nature; a new connections are appearing and old connections are disappearing all the time. However, in our algorithmic and complexity studies, we usually model social networks as static graphs. In this paper, we propose a new paradigm for the study of the well-known Target Set Selection problem, which is a fundamental problem in viral marketing and the spread of opinion through social networks. In particular, we use temporal graphs to capture the dynamic nature of social networks. We show that the temporal interpretation is, unsurprisingly, NP-complete in general. Then, we study computational complexity of this problem for multiple restrictions of both the threshold function and the underlying graph structure and provide multiple hardness lower-bounds.

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Text

Schierreich. "Maximizing Influence Spread Through a Dynamic Social Network (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27018

Markdown

[Schierreich. "Maximizing Influence Spread Through a Dynamic Social Network (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/schierreich2023aaai-maximizing/) doi:10.1609/AAAI.V37I13.27018

BibTeX

@inproceedings{schierreich2023aaai-maximizing,
  title     = {{Maximizing Influence Spread Through a Dynamic Social Network (Student Abstract)}},
  author    = {Schierreich, Simon},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16316-16317},
  doi       = {10.1609/AAAI.V37I13.27018},
  url       = {https://mlanthology.org/aaai/2023/schierreich2023aaai-maximizing/}
}