Symbolic Data Analysis (SDA) is a comprehensive approach to data analysis that focuses on symbolic data, which is defined by symbolic variables. Introduced by E. Diday in 1988, SDA has developed mainly in Europe, particularly in the fields of classification and data analysis. Unlike traditional data, which is typically single-quantitative or single-categorical, symbolic data includes multivalued, interval, or modal variables that may carry weights indicating probability or uncertainty. SDA extends traditional data analysis methods and includes various statistical measures, multivariate analysis techniques, clustering methods, and applications to databases. Recently, SDA has gained more attention due to its ability to handle complex real-world data and its advantages in processing large datasets. A small-world network is a type of graph characterized by high clustering and short average path lengths. It was first described by Watts and Strogatz, who showed that introducing random edges into a regular graph can significantly reduce the average path length while maintaining high clustering. Small-world networks are found in various contexts, including social networks, natural networks, and artificial systems. They are also associated with efficient information dissemination and resilience to attacks, reflecting a trade-off between the cost of adding edges and the efficiency of information transmission.Symbolic Data Analysis (SDA) is a comprehensive approach to data analysis that focuses on symbolic data, which is defined by symbolic variables. Introduced by E. Diday in 1988, SDA has developed mainly in Europe, particularly in the fields of classification and data analysis. Unlike traditional data, which is typically single-quantitative or single-categorical, symbolic data includes multivalued, interval, or modal variables that may carry weights indicating probability or uncertainty. SDA extends traditional data analysis methods and includes various statistical measures, multivariate analysis techniques, clustering methods, and applications to databases. Recently, SDA has gained more attention due to its ability to handle complex real-world data and its advantages in processing large datasets. A small-world network is a type of graph characterized by high clustering and short average path lengths. It was first described by Watts and Strogatz, who showed that introducing random edges into a regular graph can significantly reduce the average path length while maintaining high clustering. Small-world networks are found in various contexts, including social networks, natural networks, and artificial systems. They are also associated with efficient information dissemination and resilience to attacks, reflecting a trade-off between the cost of adding edges and the efficiency of information transmission.