Blocking Influence at Collective Level with Hard Constraints

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

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.

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