MK-Means: Detecting evolutionary communities in dynamic networks-Reference-Cited by-同舟云学术

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MK-Means: Detecting evolutionary communities in dynamic networks

Published:2021-08 Issue: Volume:176 Page:114807
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Chen Yi-Cheng,Chen Yen-Liang,Lu Jyun-Yun

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference39 articles.

1. Evolutionary clustering for mining and tracking dynamic multilayer networks;Amelio;Computational Intelligence,2016

2. Theoretical analysis of the k-means algorithm – A survey;Blömer;Algorithm Engineering,2016

3. Evolutionary clustering;Chakrabarti,2006

4. Evolutionary spectral clustering by incorporating temporal smoothness;Chi,2007

5. Survey on k-means and its variants;Choudhary;International Journal of Innovative Research in Computer and Communication Engineering,2016

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2. C3D-LSTM: a novel convolution-3D-based LSTM for link prediction in dynamic social networks;International Journal of Web and Grid Services;2024

3. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data;Information Sciences;2023-04

4. Data-driven traffic congestion patterns analysis: a case of Beijing;Journal of Ambient Intelligence and Humanized Computing;2022-09-27

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