  We present a novel software suite for social network modeling and opinion
diffusion processes. Much research on social network science has assumed
networks with static topologies. More recently, attention has been turned to
networks that evolve. Although software for modeling both the topological
evolution of networks and diffusion processes are constantly improving, very
little attention has been paid to agent modeling. Our software is designed to
be robust, modular, and extensible, providing the ability to model dynamic
social network topologies and multidimensional diffusion processes, different
styles of agent including non-homophilic paradigms, as well as a testing
environment for multi-agent reinforcement learning (MARL) experiments with
diverse sets of agent types. We also illustrate the value of diverse agent
modeling, and environments that allow for strategic unfriending. Our work shows
that polarization and consensus dynamics, as well as topological clustering
effects, may rely more than previously known on individuals' goals for the
composition of their neighborhood's opinions.
