219 citations · 707 across the 13 of their papers we have counts for
9 papers · 1 filter
Control core of undirected complex networks
Zhengzhong Yuan, Jingwen Li, Chen Zhao +2
With the development of complex networks, many researchers have paid greater attention to studying the control of complex networks over the last decade. Although some theoretical b…
Increasing trend of scientists to switch between topics
An Zeng, Zhesi Shen, Jianlin Zhou +5
We analyze the publication records of individual scientists, aiming to quantify the topic switching dynamics of scientists and its influence. For each scientist, the relations amon…
Do Mathematicians, Economists and Biomedical Scientists Trace Large Topics More Strongly Than Physicists?
Menghui Li, Liying Yang, Huina ZHang +3
In this work, we extend our previous work on largeness tracing among physicists to other fields, namely mathematics, economics and biomedical science. Overall, the results confirm…
A universal data based method for reconstructing complex networks with binary-state dynamics
Jingwen Li, Zhesi Shen, Wen-Xu Wang +2
To understand, predict, and control complex networked systems, a prerequisite is to reconstruct the network structure from observable data. Despite recent progress in network recon…
Fundamental building blocks of controlling complex networks: A universal controllability framework
Zhesi Shen, Wen-Xu Wang, Chen Zhao +1
To understand the controllability of complex networks is a forefront problem relevant to different fields of science and engineering. Despite recent advances in network controllabi…
Interrelations among scientific fields and their relative influence revealed by input-output analysis
Zhesi Shen, Liying Yang, Jiansuo Pei +7
In this paper, we try to answer two questions about any given scientific discipline: First, how important is each subfield and second, how does a specific subfield influence other…
Robust Reconstruction of Complex Networks from Sparse Data
Xiao Han, Zhesi Shen, Wen-Xu Wang +1
Reconstructing complex networks from measurable data is a fundamental problem for understanding and controlling collective dynamics of complex networked systems. However, a signifi…
Locating the source of diffusion in complex networks by time-reversal backward spreading
Zhesi Shen, Shinan Cao, Wen-Xu Wang +2
Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet t…
Reconstructing propagation networks with natural diversity and identifying hidden sources
Zhesi Shen, Wen-Xu Wang, Ying Fan +2
Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progr…