Influence maximization problem in social networks deals with finding a small subset of nodes

which could maximize the spread of influence.It has been proved that this problem is NP-hard under the commonly used diffusion models.Although many algorithms have been proposed to solve this problem approximately

it is still a challenge to guarantee the spread of influence within a low time complexity.For this

we propose a novel method based on tree-coritivity theory and give a polynomial-time algorithm

for finding the initial active nodes required in the influence maximization problem.Our algorithm considers both the structure and the propagation characteristics of a network. Moreover

by experiment

we compare this algorithm with other conventional node-selection methods such as Random

Degree and Greedy.The results demonstrate that the proposed algorithm can find the node set that can widely spread the information efficiently.