- author = "Marco Buzzanca and Vincenza Carchiolo and Alessandro Longheu and Michele Malgeri and Giuseppe Mangioni",
- title = "Evaluating the community partition quality of a network with a genetic programming approach",
- booktitle = "International Conference on Complex Networks and their Applications",
- year = "2016",
- editor = "Hocine Cherifi and Sabrina Gaito and Walter Quattrociocchi and Alessandra Sala",
- volume = "693",
- series = "Studies in Computational Intelligence",
- pages = "299--308",
- address = "Milan",
- month = "30 " # nov # " - 2 " # dec,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Community Detection, Normalize Mutual Information, Validation Function, Partition Quality",
- isbn13 = "978-3-319-50901-3",
-
DOI = "
doi:10.1007/978-3-319-50901-3_24",
- abstract = "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.",
-
notes = "Published 2017.
Complex Networks {\&} Their Applications V",
