论文标题
基于输出和输入链接的在线社交网络中的意见领导者检测
Opinion Leader Detection in Online Social Networks Based on Output and Input Links
论文作者
论文摘要
对网络中用户如何根据邻居的意见更新意见的理解引起了人们对网络科学领域的极大兴趣，并且越来越多的文献认识到了这个问题的重要性。在这篇研究论文中，我们提出了一种新的有指导网络意见形成的动态模型。在此模型中，每个节点的意见被更新为邻居意见的加权平均值，而权重代表社会影响力。我们将一种新的集中度度量定义为基于影响和整合性的社会影响度量。我们使用两个意见形成模型来衡量这种新方法：（i）Degroot模型和（ii）我们自己提出的模型。先前发表的研究研究没有考虑合规性，并且仅考虑计算社会影响时节点的影响。在我们的定义中，与高度且高度较低的节点相关的较低程度和高度的节点具有较高的中心性。作为这项研究的主要贡献，我们提出了一种在社交网络中找到一小部分节点的算法，该算法可能会对其他节点的观点产生重大影响。现实世界数据的实验表明，所提出的算法显着胜过先前发布的最新方法。
The understanding of how users in a network update their opinions based on their neighbours opinions has attracted a great deal of interest in the field of network science, and a growing body of literature recognises the significance of this issue. In this research paper, we propose a new dynamic model of opinion formation in directed networks. In this model, the opinion of each node is updated as the weighted average of its neighbours opinions, where the weights represent social influence. We define a new centrality measure as a social influence metric based on both influence and conformity. We measure this new approach using two opinion formation models: (i) the Degroot model and (ii) our own proposed model. Previously published research studies have not considered conformity, and have only considered the influence of the nodes when computing the social influence. In our definition, nodes with low in-degree and high out-degree that were connected to nodes with high out-degree and low in-degree had higher centrality. As the main contribution of this research, we propose an algorithm for finding a small subset of nodes in a social network that can have a significant impact on the opinions of other nodes. Experiments on real-world data demonstrate that the proposed algorithm significantly outperforms previously published state-of-the-art methods.
