基于GNN模型的实时转发数预测

Cheng-Ta Lo, Yi-Hsuan Lee, Jun-Hong Peng
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引用次数: 0

摘要

Twitter用户通常关心他们的推文的受欢迎程度,而转发数总是一个很好的衡量标准。随着神经网络取得的突出成就,图神经网络(Graph Neural Network, GNN)成为一个新的研究领域。在本文中,我们选择Twitter页面上的一组活跃用户。在观察它们最近的转发行为后,构建不同的GNN模型并进行标记。这些GNN模型用于预测用户早期的转发行为和估计转发数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time Retweet Count Prediction using GNN Model
Twitter users usually care about the popularity of their tweets, and retweet count is always a good measure. Along with Neural Networks have achieved outstanding accomplishments, Graph Neural Network (GNN) becomes a new research field. In this article, we select a group of active users on a Twitter page. After observing their recent retweet behaviors, different GNN models are constructed and labeled. These GNN models are used to predict the user retweet behavior and estimate the retweet count in the early stage.
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