A two-stage deep graph clustering method for identifying the evolutionary patterns of the time series of animation view counts-Reference-Cited by-同舟云学术

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A two-stage deep graph clustering method for identifying the evolutionary patterns of the time series of animation view counts

Published:2023-09 Issue: Volume:642 Page:119155
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

He Duokui,Tang Zhongjun,Chen Qianqian,Han Zhongya,Zhao Dongyuan,Sun Fengxia

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference50 articles.

1. A new shape-based clustering algorithm for time series;Li;Inf. Sci. (Ny),2022

2. DNEAT: a novel dynamic node-edge attention network for origin-destination demand prediction;Zhang;Transp. Res. Part C Emerg. Technol.,2021

3. Novel hybrid multi-head self-attention and multifractal algorithm for non-stationary time series prediction;Yu;Inf. Sci. (Ny),2022

4. An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints;Zhuang;IEEE Trans. Syst. Man Cybern. Syst.,2022

5. Improved YOLOv3 model with feature map cropping for multi-scale road object detection;Shen;Meas. Sci. Technol.,2023

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1. Disentangling Clusters from Non-Euclidean Data via Graph Frequency Reorganization;Information Sciences;2024-02

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