The largenet2 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 directed or 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) in- and out-degree distributions and the degree correlations (same-node and nearest-neighbor).
The largenet2 library has been developed by Gerd Zschaler.
It is a major rewrite of the original largenet library, which only supported undirected
networks.
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 largenet2 library has been used for simulations of neural networks and
adaptive network models of opinion formation.