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Social Structure Discovery Using Genetic Algorithm

Social Structure Discovery Using Genetic Algorithm

Saeed Nasehi Moghaddam, Mehdi Ghazanfari, Babak Teimourpour
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 26
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522513247|DOI: 10.4018/IJAMC.2017100101
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MLA

Moghaddam, Saeed Nasehi, et al. "Social Structure Discovery Using Genetic Algorithm." IJAMC vol.8, no.4 2017: pp.1-26. http://doi.org/10.4018/IJAMC.2017100101

APA

Moghaddam, S. N., Ghazanfari, M., & Teimourpour, B. (2017). Social Structure Discovery Using Genetic Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 8(4), 1-26. http://doi.org/10.4018/IJAMC.2017100101

Chicago

Moghaddam, Saeed Nasehi, Mehdi Ghazanfari, and Babak Teimourpour. "Social Structure Discovery Using Genetic Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 8, no.4: 1-26. http://doi.org/10.4018/IJAMC.2017100101

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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.

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