Social Structure Discovery Using Genetic Algorithm
| Autor: | Babak Teimourpour, Saeed Nasehi Moghaddam, Mehdi Ghazanfari |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Control and Optimization Theoretical computer science Computer science Structure (category theory) 02 engineering and technology 01 natural sciences Computer Science Applications 010104 statistics & probability Computational Mathematics Computational Theory and Mathematics Modeling and Simulation Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Decision Sciences (miscellaneous) Genetic representation 0101 mathematics |
| Zdroj: | International Journal of Applied Metaheuristic Computing. 8:1-26 |
| ISSN: | 1947-8291 1947-8283 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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