论文标题
使用归纳代表学习在社交网络中检测假新闻播放器
Detecting Fake News Spreaders in Social Networks using Inductive Representation Learning
论文作者
论文摘要
防止假新闻传播的一个重要方面是主动检测其传播的可能性。从网络分析的角度来看，在假新闻播放器检测领域的研究并未得到太多探索。在本文中，我们提出了一种基于图神经网络的方法，以识别可能成为虚假信息的播放器的节点。使用社区健康评估模型和人际关系信任，我们提出了一个归纳代表学习框架，以预测最有可能传播假新闻的密集连接的社区结构的节点，从而使整个社区容易受到感染的影响。使用现实世界Twitter网络中节点的基于拓扑和相互作用的信任属性，我们能够预测以上90％以上的虚假信息散布器。
An important aspect of preventing fake news dissemination is to proactively detect the likelihood of its spreading. Research in the domain of fake news spreader detection has not been explored much from a network analysis perspective. In this paper, we propose a graph neural network based approach to identify nodes that are likely to become spreaders of false information. Using the community health assessment model and interpersonal trust we propose an inductive representation learning framework to predict nodes of densely-connected community structures that are most likely to spread fake news, thus making the entire community vulnerable to the infection. Using topology and interaction based trust properties of nodes in real-world Twitter networks, we are able to predict false information spreaders with an accuracy of over 90%.
