ArticleScientific reports2022
Identifying influential spreaders by gravity model considering multi-characteristics of nodes.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- An Evaluation Method for Influential Nodes Based on Multi-Attribute Neighbor Contributions in Complex Networks.Entropy (Basel, Switzerland) · 2026Article
- Unified influence estimation through multi-hop reinforcement and diversity-weighted fusion in weighted complex networks.Scientific reports · 2026Article
- Unraveling hierarchical penetration mechanisms and coupling relationships of safety risks in major transportation infrastructure construction using text mining and complex networks.Scientific reports · 2026Article
- Identifying key factors in building fires: A novel approach fusing K-shell entropy gravity.PloS one · 2026Article
- Identifying influential spreaders based on improving communication transmission model and network structure.Scientific reports · 2025Article
- HA: An Influential Node Identification Algorithm Based on Hub-Triggered Neighborhood Decomposition and Asymmetric Order-by-Order Recurrence Model.Entropy (Basel, Switzerland) · 2025Article
- Finding influential nodes in complex networks based on Kullback-Leibler model within the neighborhood.Scientific reports · 2024Article
- Identifying influential nodes based on the disassortativity and community structure of complex network.Scientific reports · 2024Article
- Predicting nodal influence via local iterative metrics.Scientific reports · 2024Article
- 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 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
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2 authors.
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Abstract
How to identify influential spreaders in complex networks is a topic of general interest in the field of network science. Therefore, it wins an increasing attention and many influential spreaders identification methods have been proposed so far. A significant number of experiments indicate that depending on a single characteristic of nodes to reliably identify influential spreaders is inadequate. As a result, a series of methods integrating multi-characteristics of nodes have been proposed. In this paper, we propose a gravity model that effectively integrates multi-characteristics of nodes. The number of neighbors, the influence of neighbors, the location of nodes, and the path information between nodes are all taken into consideration in our model. Compared with well-known state-of-the-art methods, empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on ten real networks suggest that our model generally performs best. Furthermore, the empirical results suggest that even if our model only considers the second-order neighborhood of nodes, it still performs very competitively.
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