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Towards embedding information diffusion data for understanding big dynamic networks
Yang, Hong1; Zhang, Peng2; Wang, Haishuai3; Zhou, Chuan4; Li, Zhao5; Gao, Li6; Tan, Qingfeng2
Source PublicationNEUROCOMPUTING
AbstractDynamic 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.
KeywordNetwork coarsening Information diffusion Graph data mining
Indexed BySCI
Funding ProjectNSFC[61972105] ; NSFC[61872360] ; Guangdong Higher Education Innovation Group[2020KCXTD007] ; Guangzhou Higher Education Innovation Group[202032854] ; Alibaba Group through Alibaba Innovative Research Program
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000710154200014
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Document Type期刊论文
Corresponding AuthorZhang, Peng; Tan, Qingfeng
Affiliation1.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
Recommended Citation
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.
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