Multi-objective optimization approach to detecting extremal patterns in social networks

Author

Santana, Roberto

Author_Institution

Dept. of Comput. Sci. & Artificial Intell., Univ. of the Basque Country (UPV/EHU), San Sebastian, Spain

fYear

2013

fDate

15-18 Dec. 2013

Firstpage

196

Lastpage

201

Abstract

This paper introduces the use of extremal patterns as a way to characterize social networks. The concepts of Pareto-dominance, multi-objective optimization, and estimation of distribution algorithms are integrated in a general strategy to compute the multiple extremal patterns. The algorithm is applied to the identification of sets of subjects that have the broadest direct network reachability in a social network extracted from the Reality mining dataset.

Keywords

Pareto optimisation; data mining; estimation theory; network theory (graphs); reachability analysis; set theory; Pareto-dominance; direct network reachability; distribution algorithms; extremal pattern detection; multiobjective optimization approach; reality mining dataset; set identification; social networks; human dynamics; modeling of human interactions; multi-objective optimization; probabilistic modeling; social network;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies (WICT), 2013 Third World Congress on

Conference_Location

Hanoi

Type

conf

DOI

10.1109/WICT.2013.7113134

Filename

7113134

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=710035