Related references
Note: Only part of the references are listed.
Article
Mathematics, Interdisciplinary Applications
Pingle Yang et al.
Summary: Identifying key nodes that have a significant impact on network structure and function is an important issue. Existing gravity-based methods have limitations in considering the surrounding environment and assuming inter-node interactions only along the shortest path. We propose a new gravity centrality method that integrates multiple node properties and accurately characterizes node interaction distance. Numerical simulations show that our method outperforms recent high-performing node importance evaluation methods in terms of accuracy and spreading ability.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Physics, Multidisciplinary
Yanan Wang et al.
Summary: Online social networks have become crucial tools for information exchange. However, there is limited research on combining user mobility and information diffusion in these networks. This study establishes an information diffusion model considering user mobility and verifies its scientificity through theoretical analysis and simulation experiments. The results demonstrate the correlation between information diffusion, user mobility, and the range of information diffusion.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Shen Zhong et al.
Summary: The importance of research on complex networks is increasing, and identifying influential nodes remains an urgent and crucial issue. This paper proposes a Local Degree Dimension (LDD) approach that assesses the importance of nodes in complex networks by considering the increasing and decreasing rates of the numbers of each layer neighbor nodes. Experimental results demonstrate the effectiveness of LDD in accurately identifying influential nodes and quantifying their importance.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Interdisciplinary Applications
Yan Wang et al.
Summary: This article addresses the importance of identifying influential nodes in a network and proposes a new centrality measure and a heuristic algorithm for filtering propagators. Experimental results show that the new centrality measure is more accurate and effective, while the heuristic algorithm improves both spread speed and infection scale.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Biochemical Research Methods
Yusong Liu et al.
Summary: In this study, a novel module detection algorithm based on topology potential and spectral clustering algorithm was proposed for identifying co-expressed modules in gene co-expression networks. The proposed algorithm demonstrated more complete coverage of genes, balanced module sizes, and finer granularity compared to current methods, with the ability to detect modules with significant overall survival differences. Additionally, the algorithm could integrate prior knowledge for module identification, offering a more comprehensive approach for network analysis.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Information Systems
Shuyu Li et al.
Summary: This paper proposes an effective gravity model based on precise radius and value information to address the issues of identifying crucial spreaders and representing dissemination ability.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Qiuyan Shang et al.
Summary: The identification of important nodes in complex networks is a rapidly growing field, with various proposed measures needing to comprehensively consider both global and local information as well as static and dynamic data. A new gravity model with effective distance has been introduced to address this, showing effectiveness through simulation on different real networks.
INFORMATION SCIENCES
(2021)
Article
Engineering, Multidisciplinary
Yuxin Mao et al.
Summary: This paper proposes a method for predicting influential nodes in online social networks, utilizing a weighted network model and topological potential theory to evaluate node importance. Experimental results demonstrate the method's ability to predict influential nodes with a higher ratio of verified users and user coverage compared to traditional approaches.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Information Systems
Tao Wen et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Giridhar Maji et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Lingfei Li et al.
INFORMATION SCIENCES
(2020)
Article
Multidisciplinary Sciences
Zhe Li et al.
SCIENTIFIC REPORTS
(2019)
Article
Multidisciplinary Sciences
Dirk Brockmann et al.
Article
Physics, Multidisciplinary
Maksim Kitsak et al.
Article
Anthropology
MEJ Newman
