Blocking Influence at Collective Level with Hard Constraints (Student Abstract)

Zonghan Zhang, Subhodip Biswas, Fanglan Chen, Kaiqun Fu, Taoran Ji, Chang-Tien Lu, Naren Ramakrishnan, Zhiqian Chen

AAAI 2022 pp. 13115-13116

doi:10.1609/AAAI.V36I11.21694 /aaai/2022/zhang2022aaai-blocking/

Abstract

Influence blocking maximization (IBM) is crucial in many critical real-world problems such as rumors prevention and epidemic containment. The existing work suffers from: (1) concentrating on uniform costs at the individual level, (2) mostly utilizing greedy approaches to approximate optimization, (3) lacking a proper graph representation for influence estimates. To address these issues, this research introduces a neural network model dubbed Neural Influence Blocking (\algo) for improved approximation and enhanced influence blocking effectiveness. The code is available at https://github.com/oates9895/NIB.

PDF AAAI Semantic Scholar

Cite

Text

Zhang et al. "Blocking Influence at Collective Level with Hard Constraints (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21694

Markdown

[Zhang et al. "Blocking Influence at Collective Level with Hard Constraints (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/zhang2022aaai-blocking/) doi:10.1609/AAAI.V36I11.21694

BibTeX

@inproceedings{zhang2022aaai-blocking,
  title     = {{Blocking Influence at Collective Level with Hard Constraints (Student Abstract)}},
  author    = {Zhang, Zonghan and Biswas, Subhodip and Chen, Fanglan and Fu, Kaiqun and Ji, Taoran and Lu, Chang-Tien and Ramakrishnan, Naren and Chen, Zhiqian},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13115-13116},
  doi       = {10.1609/AAAI.V36I11.21694},
  url       = {https://mlanthology.org/aaai/2022/zhang2022aaai-blocking/}
}