javad hajipour

The wisdom of God in the mirror of Muslim theologians' thinking indicates His infinite knowledge. They infer God's knowledge by explaining the divine wisdom and the details of the craft and the subtleties of the verb, and they pay more attention to His current knowledge and current attributes. According to the opinion of Shiite theologians - regardless of their philosophical opinions in this matter - the definition of wisdom in the field of speech is "God does not do ugly things, which requires accepting the principle of goodness and ugliness inherent in reason; That the phenomenon in itself contains all kinds of benefits and corruptions, and the Supreme Being is also aware of these benefits and corruptions. Therefore, in his actions, he does not commit ugly things and does not abandon what is necessary. With this opinion, Shiite theologians demarcated the opinions of the Ash'arites, which deny goodness and ugliness inherent in reason.According to Ash'areh opinion, God does not perform His actions based on purity and wisdom, but what He does is wise, which they consider to be the result of God's knowledge. With this statement, they wanted to preserve the inherent exaltation of God, who does not condemn anything outside the realm of his nature, but they have prescribed the issuance of any act, even if it is futile, for his nature to be a burden. The verbs are based on expediency and wisdom and being free from cancellation and futility, but their expression of God's wisdom is similar to the wisdom of humans, which is issued from Him to achieve a purpose and goal.

Social network research analyzes the relationships between interactions, people, organizations, and entities. With the developing reputation of social media, community detection is drawing the attention of researchers. The purpose of community detection is to divide social networks into groups. These communities are made of entities that are very closely related. Communities are defined as groups of nodes or summits that have strong relationships among themselves rather than between themselves. The clustering of social networks is important for revealing the basic structures of social networks and discovering the hyperlink of systems on human beings and their interactions. Social networks can be represented by graphs where users are shown with the nodes of the graph and the relationships between the users are shown with the edges. Communities are detected through clustering algorithms. In this paper, we proposed a new clustering algorithm that takes into account the extent of relationships among people. Outcomes from particular data suggest that taking into account the profundity of people-to-people relationships increases the correctness of the aggregation methods.