Comparison of Graph Libraries

Many times, people asked me "Which is the best available graph library?", or "Which graph library should I use to compute this, or that?".
Well, personally I love to use Sage, but there are also several good alternatives. Then, the question becomes "How could we improve Sage, so that people will choose it?".

In my opinion, graph libraries are compared according to the following parameters:

1. simplicity and documentation: people have little time, and the faster they learn how to use the library, the better;
2. number of routines available;
3. speed: sometimes, the input is very big, and the algorithms take much time to finish, so that a fast implementation is fundamental.

While it is very difficult to measure the first point, the others can be compared and improved. For this reason, in order to outperform other libraries, we should implement new features, and improve existing ones. You don't say!

However, this answer is not satisfactory: in principle, we could add all features available in other libraries, but this is a huge translational work, and while we are doing this work the other libraries will change, making this effort a never-ending story.

My project proposes an alternative: cooperating instead of competing. I will try to interface Sage with other libraries, and to use their algorithms when the Sage counterpart is not available, or less efficient. This way, with an affordable amount of work, we will be able to run all algorithms available in the best graph libraries!

As a first step, I have compared all the most famous C, C++, and Python graph libraries according to points 2 and 3, in order to choose which libraries should be included. The next posts will analyze the results of this comparison.