ArticleScientific reports2021
Identifying influential spreaders in complex networks by an improved gravity model.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Targeted spreader identification via lexicographic core decomposition.Scientific reports · 2026Article
- Identifying influential nodes through hierarchical k-shell and extended neighborhood integration.Scientific reports · 2026Article
- HA: An Influential Node Identification Algorithm Based on Hub-Triggered Neighborhood Decomposition and Asymmetric Order-by-Order Recurrence Model.Entropy (Basel, Switzerland) · 2025Article
- The spatiotemporal analysis of the population migration network in China, 2021.Infectious Disease Modelling · 2023Article
- Identifying influential nodes in complex networks using a gravity model based on the H-index method.Scientific reports · 2023Article
- Identifying important nodes in complex networks based on extended degree and E-shell hierarchy decomposition.Scientific reports · 2023Article
- Identifying vital nodes for influence maximization in attributed networks.Scientific reports · 2022Article
- An influential node identification method considering multi-attribute decision fusion and dependency.Scientific reports · 2022Article
- Local-Forest Method for Superspreaders Identification in Online Social Networks.Entropy (Basel, Switzerland) · 2022Article
- An Influence Maximization Algorithm for Dynamic Social Networks Based on Effective Links.Entropy (Basel, Switzerland) · 2022Article
- Identifying influential spreaders by gravity model considering multi-characteristics of nodes.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of influential spreaders is still a challenging issue in network science. Therefore, it attracts increasing attention from both computer science and physical societies, and many algorithms to identify influential spreaders have been proposed so far. Degree centrality, as the most widely used neighborhood-based centrality, was introduced into the network world to evaluate the spreading ability of nodes. However, degree centrality always assigns too many nodes with the same value, so it leads to the problem of resolution limitation in distinguishing the real influences of these nodes, which further affects the ranking efficiency of the algorithm. The k-shell decomposition method also faces the same problem. In order to solve the resolution limit problem, we propose a high-resolution index combining both degree centrality and the k-shell decomposition method. Furthermore, based on the proposed index and the well-known gravity law, we propose an improved gravity model to measure the importance of nodes in propagation dynamics. Experiments on ten real networks show that our model outperforms most of the state-of-the-art methods. It has a better performance in terms of ranking performance as measured by the Kendall's rank correlation, and in terms of ranking efficiency as measured by the monotonicity value.
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Registered trials
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