On June 3, Wang Haiying from the Business School published a paper titled Network Spreading from Network Dimension in Physical Review Letters, a top physics journal. Wang Haiying is the unique corresponding author, Jack Murdoch Moore is the first author and he is an associate professor of the School of Physics Science and Engineering, and the Shanghai Research Institute for Intelligent Autonomous Systems, TONGJI University. Professor Yang Huijie, head of the Systems Biology Innovation Team, and Professor Gu Changgui, director of the Department of Systems Science, are the principal research members.
Network spreading is an effective theory for studying various transmission processes, such as rumor propagation on social networks, knowledge sharing in technical communities, and epidemiology in human contact networks. Accurate network spreading models can help decision-makers amplify beneficial spreading or curb harmful spreading. However, many existing models reflect only local features such as adjacency, which may perform well on simulated networks meeting specific assumptions but struggle with real network structures.In this study, the authors overcame the challenges of estimating the dimensions of real-world networks and proposed a simple but more representative spreading model based on this attribute.Compared to existing models with more parameters and higher complexity, this new spreading model demonstrated more accurate prediction capabilities on both simulated and real-world networks.
The research was sponsored by the National Natural Science Foundation of China, the Major Science and Technology Projects of Shanghai, and the Natural Science Foundation of Shanghai, etc.
The Relationship between propagation boundary, propagation distance r and network dimension D
Propagation process estimation results on different networks
With paper link：
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.237401
Translated by Jin Liang
Reviewed by Liu Weiwei
