Name
Title | ||
|---|---|---|
Evaluating the community partition quality of a network with a genetic programming approach. |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-50901-3_24 | Studies in Computational Intelligence |
DocType | Volume | ISSN |
Conference | 693 | 1860-949X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Buzzanca | 1 | 9 | 1.89 |
| Vincenza Carchiolo | 2 | 261 | 51.62 |
| Alessandro Longheu | 3 | 142 | 29.98 |
| Michele Malgeri | 4 | 219 | 42.79 |
| Giuseppe Mangioni | 5 | 199 | 37.16 |