Recently, owing to the concept of Web 2.0, many social websites have become part of our daily life.Hence, the analysis of social network attracts many researchers’ attentions. The opinion leader discoveryand influence maximization are two hot topics due to their applicability. However, due to the complexityof graph processing, it is still a challenge when handling a huge network. The efficiency is still a fatalproblem for many prior studies.In this project, we will focus on three topics, (1) Community detection: We propose an efficient andparameter-free clustering algorithm to efficiently discover the community structure of a social network. (2)Opinion leader mining: We combine the leadership-analysis-based and network-structure-based methods anddevelop a novel algorithm for efficiently mining opinion leader in a social network. (3) Influencemaximization: Based on the proposed community detection method, we utilize the community informationand reversed reachable set to efficiently find the seed set that could maximize the influence spread in a socialnetwork.Furthermore, the proposed algorithms are applied on real dataset to show the practicability andefficiency.