Blocking Influence at Collective Level with Hard Constraints (Student 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 () for improved approximation and enhanced influence blocking effectiveness. The code is available at https://github.com/oates9895/NIB.