Multi-Channel Large Network Simulation Including Adversarial Activity

Network simulation is essential to test adversarial search problems for privacy preservation and benchmarking purposes. Different generative models have been developed for single-channel, homogeneous networks that model social networks, communication, and co-authorship. Modeling multichannel networks simultaneously with correlated channel attributes at scale compounds complexity, and including adversarial signals across channels creates a second set of burdens. We present a methodology to employ a suite of generation tools to produce realistic large-scale synthetic activity graphs with embed an adversarial activity. We describe our technical process and how we employ subject matter experts (SMEs) to improve the adversarial signal. We discuss challenges in multi-channel modeling and scalability. We also discuss challenges for high fidelity multi-channel network generation at billion-edges scale.