Influence Maximization on Temporal Networks with Persistent and Reactive Behaviors
Influence Maximization (IM) aims to identify a set of \(k\) seed nodes in a network that maximizes information spread, with applications in viral marketing, epidemic containment, and behavioral targeting. While most prior work assumes static networks, real-world networks are inherently temporal and shaped by user behav...