CNetwork

CNetwork is a simple C++ class used to do network analysis and dynamics. The aim of this class is to have a single .cpp file which can be imported in any project, without having to compile separately any source code. The code runs moderately fast and allows the user to do analysis with networks of decent sizes (~10⁶).

Although it is not as complete as other libraries, such as NetworkX or igraph, it is faster than NetworkX (see benchmark below) and it is easier to code than igraph for C++. This makes CNetwork a good choice for making simple, fast code for network analysis.

Main features:

• Functions to create well-known network models, such as Erdos-Renyi, Watts-Strogatz, Barabási-Albert, lattices, or any scale-free distribution using the configuration model.
• Functions to manually add or remove nodes and links, as well as accessing them.
• Allows to have custom node properties to run dynamics over networks.
• Get node properties, such as degree, clustering, or distance between nodes.
• Import simple mtx format.
• Export your network to Graphml / Gephi compatible format to visualize it. It is possible to define custom tags or properties such as color or position.

An example of implementation

Here you have a very simple example of code that creates an Albert-Barabási network and computes the average degree. Simply download the source from the repositories, and do something like:

#include<iostream>
#include<cstdlib>

#include"CNet.cpp"

using namespace std;

int main(void)
{
    CNetwork<> net(20000); //Create a network of max size 20000 nodes
    net.create_albert_barabasi(2, 2, 21354647); //Fill this size with AB model
    cout << net.mean_degree() << endl; //Compute mean degree
}

See the wiki for more details on the use of CNetwork.

Simple benchmark

To measure the speed of CNetwork, I used the following example: create an Albert-Barabási network of one million nodes and get the largest eigenvalue.

#include<iostream>
#include<cstdlib>

#include"CNet.cpp"

using namespace std;

int main(void)
{
    CNetwork<> net(20000); //Create a network of max size 20000 nodes
    net.create_albert_barabasi(2, 2, 5464531); //Fill this size with AB model
    //compute_eigenv returns a (N+1) vector where the largest eigenvalue is the last element
    vector<double> eigenv = net.compute_eigenv(0.01);
    cout << eigenv[eigenv.size() -1] << endl; //Get the eigenvalue
}

The code above only needs 1.953 seconds (less than 2s!) to run on a moderately old computer (i3-370M processor, 2.4 GHz, from 2010). For comparison, I executed a similar code on Python's NetworkX. Only the creation of the network via

%timeit nx.barabasi_albert_graph(1000000, 2, 5464531) 

gives 16.1 seconds. Saving the network into a variable and computing its Laplacian,

%timeit nx.normalized_laplacian_matrix(barabasi)

gives 15.4 seconds, making a total of ~30 seconds.

Probably other libraries, as igraph, are able to run faster than CNetwork. However, the real advantage of CNetwork is being as simple to code as NetworkX but several times faster.