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HeDAN: Heterogeneous diffusion attention network for popularity prediction of online content

文献类型：期刊论文

作者Jia, Xueqi3,4; Shang, Jiaxing3,4; Liu, Dajiang3,4; Zhang, Haidong1,2; Ni, Wancheng1,2
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2022-10-27
卷号254页码:13
关键词Information popularity prediction Graph neural network Hierarchical attention Social network analysis Predictive factors
ISSN号0950-7051
DOI10.1016/j.knosys.2022.109659
通讯作者Shang, Jiaxing(shangjx@cqu.edu.cn) ; Ni, Wancheng(wancheng.ni@ia.ac.cn)
英文摘要Popularity prediction of online content over social media platforms is a valuable and challenging issue, the core of which lies in how to capture predictive factors from available data. However, existing studies either treat each cascade independently, which neglects the correlation among different cascades, or lack a comprehensive consideration of user behavioral proximity and preference with respect to different messages. Motivated by the above observation, this article proposes a graph neural network-based framework named HeDAN (heterogeneous diffusion attention network), which comprehensively considers various factors affecting information diffusion to provide more accurate prediction results. Specifically, we first construct a heterogeneous diffusion graph with two types of nodes (user and message) and three types of relations (friendship, interaction, and interest). Among them, friendship reflects the strength of social relationships between users, interaction reflects the behavioral proximity between users, and interest reflects user preference for information. Next, a graph neural network model with a hierarchical attention mechanism is proposed to learn from these relations. Specifically, at the node level, we utilize the graph attention network to learn the subgraph structure and generate the representations of users and messages under each specific relationship. At the semantic level, we distinguish the importance of different nodes in different relations via the multi-head self-attention mechanism and fuse them into the final prediction representation. Extensive experimental results on three real diffusion datasets show the superior performance of HeDAN over the state-of-the-art baselines. (C) 2022 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China[61966008] ; National Natural Science Foundation of China[U2033213]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000861089400016
出版者ELSEVIER
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/50416]  
专题智能系统与工程
通讯作者Shang, Jiaxing; Ni, Wancheng
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing 400044, Peoples R China
4.Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
推荐引用方式
GB/T 7714		 Jia, Xueqi,Shang, Jiaxing,Liu, Dajiang,et al. HeDAN: Heterogeneous diffusion attention network for popularity prediction of online content[J]. KNOWLEDGE-BASED SYSTEMS,2022,254:13.
APA Jia, Xueqi,Shang, Jiaxing,Liu, Dajiang,Zhang, Haidong,&Ni, Wancheng.(2022).HeDAN: Heterogeneous diffusion attention network for popularity prediction of online content.KNOWLEDGE-BASED SYSTEMS,254,13.
MLA Jia, Xueqi,et al."HeDAN: Heterogeneous diffusion attention network for popularity prediction of online content".KNOWLEDGE-BASED SYSTEMS 254(2022):13.

入库方式： OAI收割

来源：自动化研究所

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