This document proposes a new algorithm for influence maximization in social networks that combines approaches from two previous works. It first assigns influence values between users based on an analysis of their past actions, rather than random assignment. It then detects communities in the network and applies influence maximization within communities to improve computational efficiency. The algorithm is expected to provide a more accurate and faster method for this problem by leveraging credit distribution models and the relationship between community detection and viral marketing.