Finding Influencers Based on Social Interaction and Graph Structure in Social Media

With the development of online social media, influencer detection methods on these platforms have become an important area of study. However, existing influencer detection methods often place significant emphasis on the number of followers, which can lead to a drawback in maintaining the influence of users who have not been very active recently. In this paper, we propose an influencer detection method that takes both social interactions and the graph structure of social media into account. By considering both social interactions and graph structure, the proposed method prevents influence scores of users who have not been recently active from remaining disproportionately high. To demonstrate the superiority of the proposed method, we conducted a performance comparison with existing methods.