Two-Phase Genetic Algorithm for Social Network Graphs Clustering

Autoři: Mgr. Roman Neruda, Ph.D.
Rok vydání: 2013
Typ publikace: Konferenční příspěvek (mezinárodní konf.)
Periodikum: IEEE 27th International Conference on Advanced Information Networking and Applications Workshops
Projekty: Učení funkcionálních vztahů z vysoce dimenzionálních dat
Bibliografická citace: KOHOUT, J., NERUDA, Roman. Two-Phase Genetic Algorithm for Social Network Graphs Clustering. In: BAROLLI, L., XHAFA, F., TAKIZAWA, M., ENOKIDO, T., HSU, H.H., eds. IEEE 27th International Conference on Advanced Information Networking and Applications Workshops. Los Alamitos: IEEE Computer Society, 2013, S. 197-202. ISBN 978-0-7695-4952-1
Dostupné z: doi:10.1109/WAINA.2013.165
DOI: 10.1109/WAINA.2013.165
ID: 427034

Abstrakt

An important and useful task of a social network analysis is partitioning of its users into clusters. The structure of a social network can be naturally modeled by a directed graph. This approach transforms clustering of the users into searching for highly connected subgraphs in such a social network model. Many different approaches and algorithms for this problem exist, one of the possibilities is to utilize genetic algorithms for solving this type of task. In this paper, we analyze several different genetic operators and propose evolutionary based algorithm for clustering in the domain of directed weighted graphs.