Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

中文翻译：

---

可扩展的影响力最大化满足大规模社交网络的效率和有效性

影响力最大化是一个旨在选择最有影响力的节点[公式：见正文]，以最大化影响力在社交网络中的传播的问题。经典的基于贪心的算法及其改进相对缓慢或不可扩展。启发式算法的效率很快，但其准确性令人无法接受。一些算法通过消耗大量内存使用来提高准确性和效率。为了克服上述缺点，本文提出了一种快速且可扩展的影响最大化算法，称为 K-paths，它利用影响树来估计影响扩散。此外，大量实验表明，K-paths 算法在效率方面优于比较算法，同时保持了具有竞争力的准确性。