We study the problem of identifying top-K influencers when we have only local knowledge of the network structure. More specifically, the selection of top-K influencers is performed sequentially over a number of rounds. We propose an efficient algorithm called strength network similarity-based upper confidence bound (SNS_UCB1) for the identification of top-K influencers based on upper confidence bound (UCB1) from the multi-armed bandit's framework. Considering feedback in online decision-making, we rely on edge (arm) strength on falling within a large number of other edges and how edge members are similar to each other and can thus convince other users to adopt the promoted behaviours. Thus, this feedback is considered as a reward score at each pull of the arm of how likely this selection is to contribute to the increase in the cumulative reward. We evaluate the proposed algorithm under the independent cascade (IC) model on four large-scale datasets that differ in size and density. We compare our algorithm to a centrality measure-based UCB1 and several well-known state-of-the-art approaches, demonstrating its superior performance in terms of influence spread achieved with the less required time and storage space.

当我们仅对网络结构有本地知识时，我们研究了确定排名靠前的K影响者的问题。更具体地说，前K个影响者的选择是在多个回合中顺序执行的。我们提出了一种有效的算法，称为基于强度网络相似度的上限置信界（SNS_UCB1），用于基于多武装匪徒框架中的上限置信界（UCB1）来确定排名前K的影响者。考虑到在线决策中的反馈，我们依靠边缘（手臂）的力量来落入大量其他边缘，以及边缘成员如何彼此相似，从而说服其他用户采用推荐的行为。因此，该反馈被认为是每次选择都会对累积奖励的增加做出贡献的可能性的奖励分数。我们在独立级联（IC）模型下，对大小和密度不同的四个大型数据集评估了提出的算法。我们将我们的算法与基于中心度测度的UCB1和几种众所周知的最新方法进行了比较，证明了它在较少的时间和存储空间下实现的影响扩散方面具有卓越的性能。