This release presents Appearance–Behavior Framework (ABF) v2.4, extending the single-agent Digital Twin architecture of ABF v2.3 to networked multi-agent behavioral systems. The core contribution of ABF v2.4 is the Network Twin: a population-level generative framework in which multiple individualized Digital Twins are coupled through an estimated interaction matrix W. The framework introduces Multi-Agent Structural Interaction (MASI), Population-level Behavioral Transformation Risk Score (PBTRS), synthetic network recovery validation, and Network-Aware Intervention Optimization (MAIO). ABF v2.4 shifts the framework from isolated individual behavioral prediction toward collective behavioral dynamics, population-level risk propagation, and network-aware intervention allocation. The release includes the full preprint, README, and Python demo script for generating the v2.4 illustrative figures. All results are simulation-derived and illustrative. No real patient, clinical, social network, or ecological data were used. No causal claims, clinical recommendations, or policy prescriptions are implied. Real-world validation remains an essential next step.