ArticleScientific reports2016
Locating influential nodes via dynamics-sensitive centrality.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- Deciphering and steering population-level response under spatial drug heterogeneity on microhabitat structures.bioRxiv : the preprint server for biology · 2025Article
- Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics.Journal of chemical information and modeling · 2024Article
- A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network.Brain structure & function · 2023Article
- Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration.Heliyon · 2021Article
- Connectivity, reproduction number, and mobility interact to determine communities' epidemiological superspreader potential in a metapopulation network.PLoS computational biology · 2021Article
- A Temporal Network Model for Livestock Trade Systems.Frontiers in veterinary science · 2021Article
- Identifying vital nodes in complex networks by adjacency information entropy.Scientific reports · 2020Article
- Identifying influential spreaders by gravity model.Scientific reports · 2019Article
- Maximizing Network Resilience against Malicious Attacks.Scientific reports · 2019Article
- Improving short-term information spreading efficiency in scale-free networks by specifying top large-degree vertices as the initial spreaders.Royal Society open science · 2018Article
- Identification of influential spreaders in complex networks using HybridRank algorithm.Scientific reports · 2018Article
- Accurate ranking of influential spreaders in networks based on dynamically asymmetric link weights.Physical review. E · 2017Article
- Dynamic-Sensitive centrality of nodes in temporal networks.Scientific reports · 2017Article
- Identifying the perceptive users for online social systems.PloS one · 2017Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With great theoretical and practical significance, locating influential nodes of complex networks is a promising issue. In this paper, we present a dynamics-sensitive (DS) centrality by integrating topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is more accurate than degree, k-shell index and eigenvector centrality.
Identifiers
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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.