Towards embedding information diffusion data for understanding big dynamic networks

doi:10.1016/j.neucom.2021.09.024

CSpace > 应用数学研究所

	Towards embedding information diffusion data for understanding big dynamic networks
	Yang, Hong 1; Zhang, Peng 2; Wang, Haishuai 3; Zhou, Chuan4 ; Li, Zhao 5; Gao, Li 6; Tan, Qingfeng 2
	2021-11-27
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	466 页码:265-284
摘要	Dynamic networks are popularly used to describe networks that change with time. Although there have been a large number of research works on understanding dynamic networks using link prediction, node classification and community detection, there is rare work that is specially designed to address the challenge of big network size of dynamic networks. To this end, we study in this paper an emerging and challenging problem of network coarsening in dynamic networks. Network coarsening refers to a class of network "zoom-out" operations where node pairs and edges are grouped together for efficient analysis on big networks. However, existing network coarsening approaches can only handle static networks where network structure weights have been predefined before the coarsening calculation. Under the observation that big networks are highly dynamic and naturally change over time, we consider in this paper to embed information diffusion data which reflect the dynamics of networks for network coarsening. Specifically, we present a new Semi-NetCoarsen approach that jointly maximizes the likelihood of observing the information diffusion data and minimizes the network regularization with respect to the predefined network structural data. The learning function is convex and we use the accelerated proximal gradient algorithm to obtain the global optimal solution. We conduct experiments on two synthetic and five real-world data sets to validate the performance of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.
关键词	Network coarsening Information diffusion Graph data mining
DOI	10.1016/j.neucom.2021.09.024
收录类别	SCI
语种	英语
资助项目	NSFC[61972105] ; NSFC[61872360] ; Guangdong Higher Education Innovation Group[2020KCXTD007] ; Guangzhou Higher Education Innovation Group[202032854] ; Alibaba Group through Alibaba Innovative Research Program
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000710154200014
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59472
专题	应用数学研究所
通讯作者	Zhang, Peng; Tan, Qingfeng
作者单位	1.Univ Sydney, Sydney, NSW, Australia 2.Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China 3.Fairfield Univ, Comp Sci, Fairfield, CT 06430 USA 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 5.Alibaba Grp, Hangzhou, Peoples R China 6.Baidu Inc, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yang, Hong,Zhang, Peng,Wang, Haishuai,et al. Towards embedding information diffusion data for understanding big dynamic networks[J]. NEUROCOMPUTING,2021,466:265-284.
APA	Yang, Hong.,Zhang, Peng.,Wang, Haishuai.,Zhou, Chuan.,Li, Zhao.,...&Tan, Qingfeng.(2021).Towards embedding information diffusion data for understanding big dynamic networks.NEUROCOMPUTING,466,265-284.
MLA	Yang, Hong,et al."Towards embedding information diffusion data for understanding big dynamic networks".NEUROCOMPUTING 466(2021):265-284.