论文标题
预算影响最大化的近似边际分布计算方法随着延迟的影响
An Approximate Marginal Spread Computation Approach for the Budgeted Influence Maximization with Delay
论文作者
论文摘要
在本文中，我们研究了预算的影响最大化的延迟问题，文献数量有限。我们提出了一种基于近似边际扩散计算\ mbox { - }解决此问题的方法。所提出的方法已通过三个基准社交网络数据集实施，并将获得的结果与文献现有方法进行了比较。实验结果表明，所提出的方法能够选择带有合理计算时间的有影响力节点的种子节点。
In this paper, we study the Budgeted Influence Maximization with Delay Problem, for which the number of literature are limited. We propose an approximate marginal spread computation\mbox{-}based approach for solving this problem. The proposed methodology has been implemented with three benchmark social network datasets and the obtained results are compared with the existing methods from the literature. Experimental results show that the proposed approach is able to select seed nodes which leads to more number of influential nodes with reasonable computational time.
