融合级联频域特征的信息微观扩散预测模型

doi:10.16451/j.cnki.issn1003-6059.202412002

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摘要

微观扩散预测对于深入理解信息在社会网络中的传播具有关键意义.为了进一步提升信息传播预测的精准度,文中提出融合级联频域特征的信息微观扩散预测模型.首先,基于用户友谊关系和历史级联,分别构建社交图和信息扩散超图,并利用图卷积神经网络提取用户在社交关系和转发行为上的表征.然后,应用傅立叶变换,将级联的时域特征映射至频域,通过高频分量与低频分量捕捉级联的短期波动和长期趋势.最后,为了生成更具表现力的用户表示,设计注意力融合层,有效避免特征冗余和信息缺失的问题,进一步优化性能.在四个公开数据集上的实验表明,文中模型在Hits@K和mAP@K指标上都有所提升,由此表明模型的有效性.

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	赖雨阳
	朱小飞

关键词 ：社交网络, 信息传播预测, 级联预测, 傅立叶变换, 多头注意力机制

Abstract：

The research on microscopic diffusion prediction is of great significance for understanding the propagation of information in social networks. To improve the accuracy of information diffusion predictions, an information microscopic diffusion prediction model integrating cascaded frequency domain features is proposed. First, a social graph and an information diffusion hypergraph are constructed separately based on user friendship relationships and historical cascades. Graph convolutional neural networks are utilized to capture user representations in social relationships and forwarding behaviors. Next, Fourier Transform is applied to map the time-domain cascade features to the frequency domain, effectively capturing both short-term fluctuations and long-term trends in the cascade through high-frequency and low-frequency components. Finally, an attention fusion layer is designed to generate a more expressive user representation, effectively addressing the issues of feature redundancy and information loss. Thus, the performance of the proposed model is further optimized. Experiments on four public datasets show the proposed model improves Hits@K and mAP@K, demonstrating the effectiveness of itself.

Key words： Social Network Information Diffusion Prediction Cascade Prediction Fourier Transform Multi-head Attention Mechanism

收稿日期: 2024-10-15

ZTFLH:

TP 399

基金资助:

国家自然科学基金项目(No.62472059)、重庆市自然科学基金面上项目(No.CSTB2022NSCQ-MSX1672)、重庆英才计划项目(No.CSTC2024YCJH-BGZXM0022)、重庆市教育委员会科学技术研究计划重大项目(No.KJZD-M202201102)资助

通讯作者: 朱小飞,博士,教授,主要研究方向为自然语言处理、数据挖掘、信息检索.E-mail:zxf@cqut.edu.cn.
(WU Yunbing(Corresponding author), Master, associate professor. His research interests include knowledge representation and sentiment analysis.)

作者简介: 赖雨阳,硕士研究生,主要研究方向为社交网络.E-mail:laiyy@stu.cqut.edu.cn.

引用本文:

赖雨阳, 朱小飞. 融合级联频域特征的信息微观扩散预测模型[J]. 模式识别与人工智能, 2024, 37(12): 1056-1068. LAI Yuyang, ZHU Xiaofei. Information Microscopic Diffusion Prediction Integrating Cascaded Frequency Domain Features. Pattern Recognition and Artificial Intelligence, 2024, 37(12): 1056-1068.

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