Name
Abstract | ||
|---|---|---|
In the last decade the broad scope of complex networks has led to a rapid
progress. In this area a particular interest has the study of community
structures. The analysis of this type of structure requires the formalization
of the intuitive concept of community and the definition of indices of goodness
for the obtained results. A lot of algorithms has been presented to reach this
goal. In particular, an interesting problem is the search of overlapped
communities and it is field seems very interesting a solution based on the use
of genetic algorithms. The approach discusses in this paper is based on a
parallel implementation of a genetic algorithm and shows the performance
benefits of this solution. |
Year | Venue | Keywords |
|---|---|---|
2009 | Clinical Orthopaedics and Related Research | complex network,community structure,genetic algorithm,information retrieval |
Field | DocType | Volume |
Data mining,Computer science,Theoretical computer science,Artificial intelligence,Complex network,Genetic algorithm,Machine learning | Journal | abs/0912.0 |
ISSN | Citations | PageRank |
International Journal of Computer Science and Information
Security, IJCSIS, Vol. 6, No. 2, pp. 113-118, November 2009, USA | 3 | 0.44 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincenza Carchiolo | 1 | 261 | 51.62 |
| Alessandro Longheu | 2 | 142 | 29.98 |
| Michele Malgeri | 3 | 219 | 42.79 |
| Giuseppe Mangioni | 4 | 199 | 37.16 |