User behavior prediction model based on implicit links and multi-type rumor messages

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qian Li, YuFeng Xie, XinHong Wu, Yunpeng Xiao
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引用次数: 2

Abstract

Traditional prediction models of rumor forwarding are based solely on explicit network topology, and with no consideration for homogeneity and antagonism among multi-type rumor messages. To solve these problems, this study proposes a user behavior prediction model based on implicit links and multi-type rumor messages. First, because most existing studies are based on explicit network topology and ignore the influence of implicit links on information transmission, this study considers the interaction and similarity among users comprehensively and uses the K-dimension-tree algorithm to mine implicit links among non-friends, thereby improving the network topology. Second, given fuzziness and complexity of user forwarding behavior in multi-type rumor messages, considering the advantages of graph convolutional networks (GCNs) model in network representation, rumor information, user characteristics and network structure are fully represented with features. Finally, considering the high integration ability and adaptive ability of model fusion, a softmax layer is added to finalize the basic multi-classification, and then multiple GCN-based models are fused by a voting mechanism to realize the prediction of user forwarding behavior. Experiments show that the proposed model can effectively predict a user’s forwarding behavior under multi-type rumor topics, and the model has improved generalization ability.

基于隐含链接和多类型谣言消息的用户行为预测模型
传统的谣言转发预测模型完全基于显式网络拓扑结构,没有考虑多类型谣言消息之间的同质性和对抗性。为了解决这些问题,本研究提出了一种基于隐含链接和多类型谣言消息的用户行为预测模型。首先,由于现有的研究大多基于显式网络拓扑,忽略了隐式链接对信息传输的影响,因此本研究综合考虑了用户之间的交互和相似性,并使用K维树算法挖掘非好友之间的隐式链接,从而改进了网络拓扑。其次,考虑到多类型谣言消息中用户转发行为的模糊性和复杂性,考虑到图卷积网络模型在网络表示方面的优势,谣言信息、用户特征和网络结构都得到了充分的特征表示。最后,考虑到模型融合的高集成能力和自适应能力,增加了softmax层来完成基本的多分类,然后通过投票机制融合多个基于GCN的模型,实现对用户转发行为的预测。实验表明,该模型能够有效预测用户在多类型谣言话题下的转发行为,提高了泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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