Dynamic Agent-Based Network Generation

Networks are a very convenient and tractable way to model and represent interactions among entities. For example, they are often used in agent-based models to describe agents’ acquaintances. Yet, data on real-world networks are missing or difficult to gather. Being able to generate synthetic but realistic social networks is thus an important challenge in social simulation. In this article, we provide a very comprehensive and modular agent-based process of network creation. We believe that the complexity of ABM (Agent-Based Models) comes from the overall interactions of entities, but they could be kept very simple for better control over the outcome. The idea is to use an agent-based simulation to generate networks: agent behaviors are rules for the network construction. Because we want the process to be dynamic and resilient to nodes perturbation, we provide a way for behaviors to spread among agents, following the meme basic principle - spreading by imitation. Resulting generated networks are compared to a target network; the system automatically looks at the best behavior distribution to generate this specific target network