The Independent Cascade Model is a probabilistic framework for modeling how information, behaviors, or viruses spread through a network. Each newly-activated node gets one chance to activate each of its inactive neighbors with probability p. The process terminates when no new activations occur. The influence spread — the expected number of nodes activated from a seed set — is #P-hard to compute exactly but is submodular, enabling greedy approximation algorithms with a (1 − 1/e) guarantee (Kempe, Kleinberg & Tardos 2003). Applications include viral marketing, epidemic modeling, and misinformation containment.