Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space-Reference-Cited by-同舟云学术

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Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space

Published:2024-08-24 Issue: Volume:530 Page:1655-1666
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Li Ruikun¹^ORCID,Wang Huandong²^ORCID,Piao Jinghua²^ORCID,Liao Qingmin¹^ORCID,Li Yong²^ORCID

Affiliation:

1. Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

2. Department of Electronic Engineering BNRist, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671968

Reference56 articles.

1. Pedro Almagro, Marián Bogu ná, and M Ángeles Serrano. 2022. Detecting the ultra low dimensionality of real networks. Nature communications, Vol. 13, 1 (2022), 6096.

2. Alex Arenas, Albert Díaz-Guilera, Jurgen Kurths, Yamir Moreno, and Changsong Zhou. 2008. Synchronization in complex networks. Physics reports, Vol. 469, 3 (2008), 93--153.

3. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804--17815.

4. Albert-László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. science, Vol. 286, 5439 (1999), 509--512.

5. Baruch Barzel and Albert-László Barabási. 2013. Universality in network dynamics. Nature physics, Vol. 9, 10 (2013), 673--681.

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