.png){kind=icon}
返回
GAN-based deep learning framework of network reconstruction
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-022-00893-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Inferring the topology of a network from network dynamics is a significant problem with both theoretical research significance and practical value. This paper considers how to reconstruct the network topology according to the continuous-time data on the network. Inspired by the generative adversarial network(GAN), we design a deep learning framework based on network continuous-time data. The framework predicts the edge connection probability between network nodes by learning the correlation between network node state vectors. To verify the accuracy and adaptability of our method, we conducted extensive experiments on scale-free networks and small-world networks at different network scales using three different dynamics: heat diffusion dynamics, mutualistic interaction dynamics, and gene regulation dynamics. Experimental results show that our method significantly outperforms the other five traditional correlation indices, which demonstrates that our method can reconstruct the topology of different scale networks well under different network dynamics.
Keyword:
Network reconstruction
Generative adversarial network
Network topology
Network dynamics
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
机构
引用论文
.png){kind=icon}
The Effect of 40-Hz Light Therapy on Amyloid Load in Patients with Prodromal and Clinical Alzheimer’s Disease40Hz光疗法对前驱和临床阿尔茨海默病患者淀粉样蛋白负荷的影响
OAAI
OAAI分享收藏分享收藏Public Roles for the Medical Profession in the United States: Beyond Theories of Decline and FallOAAI
OAAI分享收藏
查看更多内容