ArticleScientific reports2019
Identifying and quantifying potential super-spreaders in social networks.
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 12 papers.
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Who cites it
12 citing papers in PubMed, 48 citations in OpenAlex.
- Understanding the role of international flight networks in disease spread: spatial epidemic prevention zones and hierarchical disease control policies.International journal of health geographics · 2026Article
- Article
- Network Analysis of Small Ruminant Movements in Uganda: Implications for Control of Transboundary Animal Diseases.Transboundary and emerging diseases · 2025Article
- Key node identification for a network topology using hierarchical comprehensive importance coefficients.Scientific reports · 2024Article
- Dynamic identification of important nodes in complex networks based on the KPDN-INCC method.Scientific reports · 2024Article
- Identifying Potential Super-Spreaders and Disease Transmission Hotspots Using White-Tailed Deer Scraping Networks.Animals : an open access journal from MDPI · 2023Article
- Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism.Infection control and hospital epidemiology · 2023Article
- Analysis of patterns of livestock movements in the Cattle Corridor of Uganda for risk-based surveillance of infectious diseases.Frontiers in veterinary science · 2023Article
- Identifying influential spreaders in complex networks for disease spread and control.Scientific reports · 2022Article
- Quantifying agent impacts on contact sequences in social interactions.Scientific reports · 2022Article
- Connectivity, reproduction number, and mobility interact to determine communities' epidemiological superspreader potential in a metapopulation network.PLoS computational biology · 2021Article
- Spatial super-spreaders and super-susceptibles in human movement networks.Scientific reports · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantifying the nodal spreading abilities and identifying the potential influential spreaders has been one of the most engaging topics recently, which is essential and beneficial to facilitate information flow and ensure the stabilization operations of social networks. However, most of the existing algorithms just consider a fundamental quantification through combining a certain attribute of the nodes to measure the nodes' importance. Moreover, reaching a balance between the accuracy and the simplicity of these algorithms is difficult. In order to accurately identify the potential super-spreaders, the CumulativeRank algorithm is proposed in the present study. This algorithm combines the local and global performances of nodes for measuring the nodal spreading abilities. In local performances, the proposed algorithm considers both the direct influence from the node's neighbourhoods and the indirect influence from the nearest and the next nearest neighbours. On the other hand, in the global performances, the concept of the tenacity is introduced to assess the node's prominent position in maintaining the network connectivity. Extensive experiments carried out with the Susceptible-Infected-Recovered (SIR) model on real-world social networks demonstrate the accuracy and stability of the proposed algorithm. Furthermore, the comparison of the proposed algorithm with the existing well-known algorithms shows that the proposed algorithm has lower time complexity and can be applicable to large-scale networks.
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