ArticleScientific reports2017
Dynamic-Sensitive centrality of nodes in temporal networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Causal disconnectomics of motion perception networks: insights from transcranial magnetic stimulation-induced BOLD responses.The Journal of physiology · 2026Article
- Temporal network instability and low-frequency overconnectivity underlie disorders of consciousness in severe brain injury.Scientific reports · 2025Article
- Deciphering and steering population-level response under spatial drug heterogeneity on microhabitat structures.bioRxiv : the preprint server for biology · 2025Article
- Modeling and simulation on the spreading dynamics of public opinion information in temporal group networks.Scientific reports · 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
- A Temporal Network Model for Livestock Trade Systems.Frontiers in veterinary science · 2021Article
- Temporal evolution of hydroclimatic teleconnection and a time-varying model for long-lead prediction of Indian summer monsoon rainfall.Scientific reports · 2018Article
- Fractal and multifractal analyses of bipartite networks.Scientific reports · 2017Article
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Authors and funding
2 authors.
Funding
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Abstract
Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold b
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Registered trials
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