A Soft Computing-Based Approach to Group Relationship Analysis Using Weighted Arithmetic and Geometric Mean

Relationships patterns between social entities in the social network are the main attribute that plays important role in our lives. They are mostly complex in nature and uncertain to find out. To quantify these relationships, patterns is a very potent issue in social networks analysis. This paper proposes a robust function that finds the relationships between groups of finite size based on fuzzy graphs theory. The relationship among elements in-group is found out by using the arithmetic mean or geometric mean. This paper has taken advantages of both weighted arithmetic and geometric mean, which combines the advantage of both arithmetic and geometric mean. The weights taken are the function of the importance of both the social elements participating in a term. These weights can be the parameters like the betweenness centrality or closeness centrality.