This document summarizes a research paper that proposes a new algorithm for influence maximization in social networks. The algorithm draws inspiration from previous works on community detection and a data-based credit distribution model. It first assigns credits to users based on their past actions to determine probabilistic influence between users. It then uses a community detection approach to identify groups of similar users before applying an influence maximization algorithm based on the independent cascade model. The proposed approach aims to better learn mutual influence from user data and improve time complexity by leveraging the relationship between community detection and viral marketing.