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

doi:10.1016/j.knosys.2022.109659

	HeDAN: Heterogeneous diffusion attention network for popularity prediction of online content
	Jia, Xueqi 1,2; Shang, Jiaxing 1,2; Liu, Dajiang 1,2; Zhang, Haidong3,4 ; Ni, Wancheng3,4
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
	2022-10-27
卷号	254 页码:13
通讯作者	Shang, Jiaxing(shangjx@cqu.edu.cn) ; Ni, Wancheng(wancheng.ni@ia.ac.cn)
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.
关键词	Information popularity prediction Graph neural network Hierarchical attention Social network analysis Predictive factors
DOI	10.1016/j.knosys.2022.109659
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61966008] ; National Natural Science Foundation of China[U2033213]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000861089400016
出版者	ELSEVIER
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50417
专题	复杂系统认知与决策实验室_智能系统与工程
通讯作者	Shang, Jiaxing; Ni, Wancheng
作者单位	1.Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China 2.Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing 400044, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
通讯作者单位	中国科学院自动化研究所
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.