Blocking Influence at Collective Level with Hard Constraints (Student Abstract)
Keywords:Influence Blocking., Social Network., Infectious Disease., Neural Network., Machine Learning., Hard Constraint.
AbstractInfluence 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.
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
AAAI Student Abstract and Poster Program