Influence Maximization (IM) is an important issue in network analyzing which widely occurs in social networks. The IM problem aims to detect the top-k influential seed nodes that can maximize the influence spread. Although a lot of studies have been performed, a novel algorithm with a better balance between time-consumption and guaranteed performance is still needed. In this work, we present a novel algorithm called MLIM for the IM problem, which adopts maximum likelihood-based scheme under the Independent Cascade(IC) model. We construct thumbnails of the social network and calculate the L-value for each vertex using the maximum likelihood criterion. A greedy algorithm is proposed to sequentially choose the seeds with the smallest L-value. Empirical results on real-world networks have proved that the proposed method can provide a wider influence spreading while obtaining lower time consumption.

影响力最大化（IM）是网络分析中的一个重要问题，在社交网络中广泛存在。IM问题旨在检测可以最大化影响扩散的前k个有影响力的种子节点。尽管已进行了大量研究，但仍需要一种在时间消耗和性能保证之间达到更好平衡的新颖算法。在这项工作中，我们提出了一种用于IM问题的称为MLIM的新颖算法，该算法在Independent Cascade（IC）模型下采用了基于最大似然性的方案。我们构造社交网络的缩略图，并使用最大似然准则为每个顶点计算L值。提出了一种贪婪算法来依次选择L最小的种子-值。实际网络上的经验结果证明，该方法可以提供更广泛的影响传播，同时获得较低的时间消耗。