The spreading of negative influence, such as epidemic, rumor, false information and computer virus, may lead to serious consequences in social networks. The issue of negative influence blocking maximization arouses intense interest of the researchers. However, in the real world social network environment, the exact source of negative influence is usually unknown. In most cases, we only know the distribution of negative seeds, which is the probability for each node to be a negative seed. In this work, we investigate the problem of maximizing the blocking on negative influence from uncertain sources. We propose the competitive influence linear threshold propagation model (CI-LTPM) for the problem. Based on the IC-LTPM model, we define the problem of uncertain negative source influence blocking maximization (UNS-IBM). We use the propagation tree in the live-edge (LE) sub-graph for estimating the influence propagation. An algorithm is proposed to calculate the blocking increments of the positive seeds based on the propagation tree in the LE sub-graph. We observed that the blocking effect of the positive seeds is the reduction on the negative influence after the positive seeds and their related edges being deleted from the LE sub-graph. Based on such observation, we propose a node deletion-based algorithm NDB (node-deletion-blocking) for solving the UNS-IBM problem. Our experiment results show that NDB can block more negative influence in less computational time than other methods.

流行病、谣言、虚假信息和计算机病毒等负面影响的传播可能会在社交网络中导致严重后果。负面影响阻止最大化的问题引起了研究人员的强烈兴趣。然而，在现实世界的社交网络环境中，负面影响的确切来源通常是未知的。大多数情况下，我们只知道负种子的分布，即每个节点成为负种子的概率。在这项工作中，我们研究了最大化阻止来自不确定来源的负面影响的问题。我们针对该问题提出了竞争影响线性阈值传播模型（CI-LTPM）。基于IC-LTPM模型，我们定义了不确定负源影响阻塞最大化问题（UNS-IBM）。我们使用实时边缘（LE）子图中的传播树来估计影响传播。提出了一种基于LE子图中的传播树计算正种子的块增量的算法。我们观察到正种子的阻塞效应是在正种子及其相关边从 LE 子图中删除后负面影响的减少。基于这样的观察，我们提出了一种基于节点删除的算法 我们观察到正种子的阻塞效应是在正种子及其相关边从 LE 子图中删除后负面影响的减少。基于这样的观察，我们提出了一种基于节点删除的算法 我们观察到正种子的阻塞效应是在正种子及其相关边从 LE 子图中删除后负面影响的减少。基于这样的观察，我们提出了一种基于节点删除的算法NDB（节点删除阻塞）用于解决 UNS-IBM 问题。我们的实验结果表明，与其他方法相比，NDB可以在更少的计算时间内阻止更多的负面影响。