Dynamics in and dynamics of networks using DyNSimF

Abstract

Advances in formal theories, network science, and data collection technologies make complex-agent networks and adaptive networks increasingly powerful tools in the fields’ of complexity science and computational social science. We present DyNSimF; an open source package that facilitates the modelling of adaptive networks, capturing complex interacting dynamics on a network as well as dynamics of (the structure of) a network. Capable of complex agent-based simulations on a dynamic network, it is able to capture individual-level dynamics as well as dynamics of the network structure, and how these interact and evolve. By capturing the emergent behaviour resulting from the interactions of node states and network topology, we argue that DyNSimF will help modellers to gain a fundamentally better understanding of complex network systems. The package can handle both weighted and directional links, is computationally scalable and efficient, and includes a generic utility-based edge selection framework. DyNSimF provides a generic modelling framework for dynamics networks and includes visualisation methods and tools to aid in the analysis of models. It is designed to be extensible and aims to be easy to learn and work with, allowing non-experts to focus on model development, while being highly customisable and extensible to allow for complex custom models.