Event Prediction in Online Social Networks

J. Data Intell. Pub Date : 2021-03-01 DOI:10.26421/JDI2.1-4
Leonard Tan, Thuan Pham, Hang Kei Ho, Tan Seng Kok
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引用次数: 2

Abstract

Event prediction is a very important task in numerous applications of interest like fintech, medical, security, etc. However, event prediction is a highly complex task because it is challenging to classify, contains temporally changing themes of discussion and heavy topic drifts. In this research, we present a novel approach which leverages on the RFT framework developed in \cite{tan2020discovering}. This study addresses the challenge of accurately representing relational features in observed complex social communication behavior for the event prediction task; which recent graph learning methodologies are struggling with. The concept here, is to firstly learn the turbulent patterns of relational state transitions between actors preceeding an event and then secondly, to evolve these profiles temporally, in the event prediction process. The event prediction model which leverages on the RFT framework discovers, identifies and adaptively ranks relational turbulence as likelihood predictions of event occurrences. Extensive experiments on large-scale social datasets across important indicator tests for validation, show that the RFT framework performs comparably better by more than 10\% to HPM \cite{amodeo2011hybrid} and other state-of-the-art baselines in event prediction.
在线社交网络中的事件预测
事件预测在金融科技、医疗、安全等众多应用中是一项非常重要的任务。然而,事件预测是一项非常复杂的任务,因为分类具有挑战性,包含暂时变化的讨论主题和沉重的主题漂移。在这项研究中,我们提出了一种利用\cite{tan2020discovering}中开发的RFT框架的新方法。本研究解决了在事件预测任务中准确表征观察到的复杂社会交际行为的关系特征的挑战;这是最近的图形学习方法正在努力解决的问题。这里的概念是,首先学习事件发生前参与者之间关系状态转换的湍流模式,然后,在事件预测过程中,在时间上演变这些概况。利用RFT框架的事件预测模型发现、识别并自适应地将关系湍流排序为事件发生的可能性预测。在大型社会数据集上进行的大量实验表明,RFT框架在事件预测方面的表现比HPM \cite{amodeo2011hybrid}和其他最先进的基线要好10%以上。
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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