Subhodip Biswas, Fanglan Chen, Kaiqun Fu, Taoran Ji, Naren Ramakrishnan, Zhiqian Chen


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

Zonghan Zhang, Subhodip Biswas, Fanglan Chen, Kaiqun Fu, Taoran Ji, Chang-Tien Lu, Naren Ramakrishnan, Zhiqian Chen:Blocking Influence at Collective Level with Hard Constraints (Student Abstract). AAAI2022: 13115-13116


Kaiqun Fu

Naren Ramakrishnan

Taoran Ji

Fanglan Chen

Publication Details

Date of publication:
June 28, 2022
AAAI Conference on Artificial Intelligence
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