A C++ library for the efficient simulation of large dynamic network models

The largenet library is a collection of C++ classes providing a framework for the simulation of large discrete adaptive networks. It provides data structures for an in-memory representation of undirected networks, in which every node and link can have an integer-valued state.

Efficient access to (random) nodes and links as well as (random) nodes and links with a given state value is provided. A limited number of graph-theoretical measures is implemented, such as the (state-resolved) degree distributions, the clustering coefficient, the nearest-neighbor degree correlations, and the average shortest path length.

The largenet library has been developed mostly by Gerd Zschaler based on original ideas of Thilo Gross. Most of this work is licensed under the Creative Commons CC-BY-NC license. See COPYING for details.

For installation instructions, see INSTALL. For examples how to use the library, see the examples directory.

The largenet library has been used for simulations of evolutionary games on graphs, adaptive network models of swarming, fish schooling, epidemiological models, and opinion formation models.