Agent-based modeling (ABM) is a scientific method used in fields such as social science, biology, and ecology to simulate interactions of autonomous agents and study the resulting emergent phenomena. The relationships between agents, which structure the simulated interactions, are often represented by network graphs. Since empirical data on networks is rare, many agent-based models rely on artificially generated networks (Amblard et al., 2015). Consequently,
generating a valid network structure at the beginning of a simulation is critical. Additionally, accessing and modifying the network during the simulation are steps that must be managed in almost any agent-based model.
Pop2net is a Python package that combines many steps related to network generation and management for ABM using a bipartite approach. Bipartite networks consist of two distinct types of entities where edges are only formed between entities of different types. In Pop2net, relationships are represented as bipartite networks connecting actors and locations. When two actors are linked to the same location, they are considered connected through that shared
location. In this way, locations serve as a contact layer between actors, representing places where interactions occur or contexts that facilitate actor connections. The aim of Pop2net’s bipartite approach to relations is to simplify the generation and management of realistic network structures in ABM.