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Competitive Influence Blocking in Online Social Networks: A Case Study on WeChat

机译：在线社交网络中的竞争影响力阻断：以微信为例

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摘要

Rapid development of online social networks further facilitates the expansion of information over the Internet. In many scenarios, several pieces of different or even opposite information could diffuse competitively at the same time. Recently, some messenger APPs such as WeChat arose, where users can send links in their "WeChat Moments (WM)", which is called the messenger-based social network (Msg-SN). The unique characteristics in Msg-SN may lead to great difference on the network topology compared to conventional social networks. It is impossible to find the key opinion leaders with millions of followers to help block the rumors or to locate the source of rumors. Thus, the business often tries to select and inject a set of users in the network with the truth to block negative influence diffusion. We call this problem as the competitive influence blocking (CIB) problem. In this paper, we study the CIB problem in Msg-SN under the competitive linear threshold (CLT) model. We propose a fast heuristic algorithm based on eigenvector centrality to optimize the negative influence reduction by selecting a positive seed set. The experimental results using real-world WeChat Moments data show that our algorithm performs better than the effective CLDAG algorithm and also runs faster.
机译：在线社交网络的快速发展进一步促进了Internet上信息的扩展。在许多情况下，几条不同甚至相反的信息都可以同时竞争地扩散。最近，出现了一些Messenger App，例如WeChat，用户可以在其“ WeChat Moments（WM）”中发送链接，该链接称为基于Messenger的社交网络（Msg-SN）。与常规社交网络相比，Msg-SN中的独特特征可能导致网络拓扑上的巨大差异。不可能找到拥有数百万追随者的关键意见领袖来帮助阻止谣言或找到谣言的来源。因此，企业经常试图用事实选择并注入网络中的一组用户，以阻止负面影响的扩散。我们将此问题称为竞争影响阻止（CIB）问题。在本文中，我们研究了竞争线性阈值（CLT）模型下Msg-SN中的CIB问题。我们提出了一种基于特征向量中心度的快速启发式算法，以通过选择正种子集来优化负面影响减少。使用真实世界的微信矩数据进行的实验结果表明，我们的算法比有效的CLDAG算法执行得更好，并且运行速度更快。

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