An Improved Degree Discount Approach for Influence Maximization in Social Networks

In the influence maximization detection algorithm, the degree discount algorithm is an efficient heuristic algorithm. Aiming at the shortcomings of the degree discount algorithm, the formula for calculating the expected influence is modified and the first-order improved degree discount algorithm is proposed. Furthermore, in order to ensure the seed nodes are scattered in the network, a redundancy weakening mechanism is introduced and then the second-order improved degree discount algorithm is constructed. Based on the independent cascade model, the proposed algorithms are compared with other algorithms in four real networks. The experimental results confirm that the proposed algorithms can ensure faster and wider information spreading with low time complexity.