dynamic networks embedding

本文综述了动态社区检测与网络嵌入领域的最新研究进展,包括动态三角、元路径向量、动态网络嵌入等算法。通过引用8至15篇关键文献,探讨了统计聚类、检测阈值、脑动态估计、社交动态社区检测等主题,并提供了多个GitHub项目链接供读者深入研究。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

2018AAAI  https://github.com/luckiezhou/DynamicTriad

https://github.com/linhongseba/Temporal-Network-Embedding

meta2vec https://github.com/PearlPeiyanYu/Dynamic-Network-Embedding

PNAS参考文献8-15 找源代码及其数据集做测试与训练

https://github.com/CSIRMDS/dynamic_community_detection

https://github.com/meanis/Artificial_Bee_Colony_Optimization_for_dynamic_community_detection

https://github.com/itavares/Temporal-Link-Prediction-and-Community-Detection

关键词  gynamic community detection 

temporal community detection

gynamic network clustring

temporal network clustring

8. Matias C, Miele V (2016) Statistical clustering of temporal networks through a dynamic stochastic block model. J R Stat Soc Ser B Stat Methodol 79:1119–1141.

9. Ghasemian A, Zhang P, Clauset A, Moore C, Peel L (2016) Detectability thresholds and optimal algorithms for community structure in dynamic networks. Phys Rev X 6:031005.

10. Cribben I, Yu Y (2017) Estimating whole-brain dynamics by using spectral clustering. J R Stat Soc Ser C Appl Stat 66:607–627.

11. Nguyen NP, Dinh TN, Shen Y, Thai MT (2014) Dynamic social community detection and its applications. PLoS One 9:e91431.

12. Xu KS, Hero AO (2014) Dynamic stochastic blockmodels for time-evolving social networks. IEEE J Selected Top Signal Process 8:552–562.

13. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328: 876–878.

14. Bazzi M, et al. (2016) Community detection in temporal multilayer networks, with an application to correlation networks. Multiscale Model Simul 14:1–41.

15. Bassett DS, et al. (2013) Robust detection of dynamic community structure in networks. Chaos 23:013142.

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值