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 https://github.com/oates9895/NIB.
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
- Date of publication:
- June 28, 2022
- AAAI Conference on Artificial Intelligence
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