.png){kind=icon}
返回
Structure-oriented prediction in complex networks
.png){kind=icon}
OA
AI
DOI：10.1016/j.physrep.2018.05.002.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this article, we aim to review different network related prediction problems, summarize and classify relevant prediction methods, analyze their advantages and disadvantages, and point out the forefront as well as critical challenges of the field. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Complex networks
Prediction
Network structure
Network dynamics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
29.5
论文数:
3.0K
被引数:
3.8W
机构
引用论文
<strong>A new species of <em>Echidnodella</em> (Asterinales, Lembosiaceae) from Western Ghats of Kerala State, India </strong>.png)
Phytotaxa
PhytotaxaIF0
PREAI
分享收藏
查看更多内容