Utilizing Degree Centrality Measures for Product Advertisement in Social Networks

Social networks, as abstract representations of relationships between entities, play a pivotal role in connecting individuals in the digital age. This paper delves into the realm of social network analysis (SNA), a method rooted in graph theory that explores the dynamics of social relationships within communities. One of the key objectives of SNA is to identify the most influential actors within a social network, a task often achieved by calculating various centrality metrics. These metrics, such as degree centrality, allow to quantify the significance and impact of individual nodes within a social network. In the context of marketing and brand promotion, these metrics are particularly relevant and useful. Leveraging social networks for marketing endeavors can enhance brand recognition and foster customer loyalty. When promoting products within social networks, targeting the “most important” members (i.e., those with higher centrality metrics) can exponentially increase the reach and impact of the marketing campaign. In this paper, an approach is suggested for supporting social network marketing by employing binary logistic regression analysis. Logistic regression is a valuable tool in case of models where the dependent variable is dichotomous, and it can be an ideal method for predicting node behavior in the context of a product promotion campaign. By analyzing the actions of the “most important” nodes within social networks, we can predict which nodes are likely to purchase a marketed product based on their interactions and centrality.