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

Authors

  • Zonghan Zhang Mississippi State University, Department of Computer Science and Engineering
  • Subhodip Biswas Virginia Tech
  • Fanglan Chen Virginia Tech
  • Kaiqun Fu South Dakota State University
  • Taoran Ji Virginia Tech
  • Chang-Tien Lu Virginia Tech
  • Naren Ramakrishnan Virginia Tech
  • Zhiqian Chen Mississippi State University, Department of Computer Science and Engineering

DOI:

https://doi.org/10.1609/aaai.v36i11.21694

Keywords:

Influence Blocking., Social Network., Infectious Disease., Neural Network., Machine Learning., Hard Constraint.

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.
AAAI-22 Proceedings Cover

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Published

2022-06-28

How to Cite

Zhang, Z., Biswas, S., Chen, F., Fu, K., Ji, T., Lu, C.-T., Ramakrishnan, N., & Chen, Z. (2022). Blocking Influence at Collective Level with Hard Constraints (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13115-13116. https://doi.org/10.1609/aaai.v36i11.21694

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program