New prediction method for data spreading in social networks based on machine learning algorithm
New prediction method for data spreading in social networks based on machine learning algorithm
复制标题
基于机器学习算法的社交网络数据传播预测新方法
DOI：
10.12928/telkomnika.v18i6.16300
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Seifedine Kadry
中科院分区：
文献类型：
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作者：
M. N. Meqdad;Rawya Al;Seifedine Kadry
信息扩散预测是研究新闻、信息或主题在结构化数据（如图表）中的传播路径。该领域的研究主要集中在两个目标上：跟踪信息扩散路径和寻找决定未来下一条路径的成员。传统方法在这一领域的主要问题是使用简单的概率方法，而不是智能方法。近年来，人们对机器学习算法在这一领域的应用越来越感兴趣。近年来，深度学习作为机器学习的一个分支，在信息扩散预测领域得到了越来越多的应用。本文提出了一种基于图神经网络算法的机器学习方法，该方法涉及根据给定科学主题中相邻的活动顶点选择非活动顶点进行激活。基本上，在该方法中，通过活动顶点激活非活动顶点来预测信息扩散路径。该方法在三个科学书目数据集上进行了测试：数字书目和图书馆项目（DBLP）、Pubmed和Cora。这种方法试图回答这样一个问题：谁将是某一特定科学领域下一篇文章的出版商。与其他方法相比，该方法在DBL和Pubmed数据集上的精度分别提高了10%和5%。
Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.