Evaluating the community partition quality of a network with a genetic programming approach

Although the problem of partition quality evaluation is well-known in literature, most of the traditional approaches involve the application of a model built upon a theoretical foundation and then applied to real data. Conversely, this work presents a novel approach: it extracts a model from a network which partition in ground-truth communities is known, so that it can be used in other contexts. The extracted model takes the form of a validation function, which is a function that assigns a score to a specific partition of a network: the closer the partition is to the optimal, the better the score. In order to obtain a suitable validation function, we make use of genetic programming, an application of genetic algorithms where the individuals of a population are computer programs. In this paper we present a computationally feasible methodology to set up the genetic programming run, and show our design choices for the terminal set, function set, fitness function and control parameters.