ArticleScientific reports2019
Identifying influential spreaders by gravity model.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 244 citations in OpenAlex.
- Targeted spreader identification via lexicographic core decomposition.Scientific reports · 2026Article
- Identifying influential nodes through hierarchical k-shell and extended neighborhood integration.Scientific reports · 2026Article
- Identifying key factors in building fires: A novel approach fusing K-shell entropy gravity.PloS one · 2026Article
- 3V-GM: A Tri-Layer "Point-Line-Plane" Critical Node Identification Algorithm for New Power Systems.Entropy (Basel, Switzerland) · 2025Article
- Self-consistent gravity model for inferring node mass in flow networks.Scientific reports · 2025Article
- Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model.Entropy (Basel, Switzerland) · 2025Article
- 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
- Identifying influential nodes in complex networks using a gravity model based on the H-index method.Scientific reports · 2023Article
- A mechanics model based on information entropy for identifying influencers in complex networks.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Identifying influential spreaders by gravity model considering multi-characteristics of nodes.Scientific reports · 2022Article
- Identifying influential spreaders in complex networks for disease spread and control.Scientific reports · 2022Article
- Article
- A new model to identify node importance in complex networks based on DEMATEL method.Scientific reports · 2021Article
- Identifying influential spreaders in complex networks by an improved gravity model.Scientific reports · 2021Article
- Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration.Heliyon · 2021Article
- Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks.PloS one · 2021Article
- Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease.Frontiers in cardiovascular medicine · 2021Article
- Identifying Influencers in Social Networks.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node's importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.