Social Structure Discovery Using Genetic Algorithm

Saeed Nasehi Moghaddam, Mehdi Ghazanfari and Babak Teimourpour
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Saeed Nasehi Moghaddam: University of Zanjan, Zanjan, Iran
Mehdi Ghazanfari: Iran University of Science and Technology, Tehran, Iran
Babak Teimourpour: Department of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

International Journal of Applied Metaheuristic Computing (IJAMC), 2017, vol. 8, issue 4, 1-26

Abstract: As a way of simplifying, size reducing and making the structure of each social network be comprehensible, blockmodeling consists of two major, essential components: partitioning of actors to equivalent classes, called positions, and clarifying relations between and within positions. While actor partitioning in conventional blockmodeling is performed by several equivalence definitions, generalized blockmodeling, searches, locally, the best partition vector that best satisfies a predetermined structure. The need for known predefined structure and using a local search procedure, makes generalized blockmodeling be restricted. In this paper, the authors formulate blockmodel problem and employ a genetic algorithm for to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and situations such as dichotomous, signed, ordinal and interval valued, and multiple relations, the quality of results shows better fitness than classic and generalized blockmodeling.

Date: 2017
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