This thesis introduced the solution of influence maximization and analyzed the advantages and disadvantages of those solutions. After studying the weak tie in social network, it is found that weak ties can effectively break the information barriers between different societies in social network and make information circulate in different societies. Making use of the weak tie's advantages, this thesis proposes a new solution, the BWTG algorithm, based on the greedy thought to resolve influence maximization problem. According to different solution spaces, the BWTG algorithm is divided into two different types:BCWTG and BNCWTG algorithm. There are two traditional evaluation indexes of influence maximization problem, namely, time complexity and the final activated nodes number. But considering the practical situation, a new evaluation index named ANNI is proposed to measure the ratio of profit and pay. Besides, in order to verify the performance of the proposed algorithm, different scales and types of data are used to carry out the experiment. The time complexity, the final activated nodes number and ANNI are compared with the classical Greedy algorithm. The experimental result finds that BCWTG and BNCWTG algorithm have lower time complexity and higher ANNI, but lower final activated nodes number than Greedy algorithm. But under some certain conditions, BCWTG and BNCWTG can be almost equal to Greedy in activated nodes number.