Epidemic centrality — is there an underestimated epidemic impact of network peripheral nodes?

@article{iki2013EpidemicC,
  title={Epidemic centrality — is there an underestimated epidemic impact of network peripheral nodes?},
  author={Mile {\vS}iki{\'c} and Alen Lancic and Nino Antulov-Fantulin and Hrvoje Stefancic},
  journal={The European Physical Journal B},
  year={2013},
  volume={86},
  pages={1-13}
}
AbstractIn the study of disease spreading on empirical complex networks in SIR model, initially infected nodes can be ranked according to some measure of their epidemic impact. The highest ranked nodes, also referred to as “superspreaders”, are associated to dominant epidemic risks and therefore deserve special attention. In simulations on studied empirical complex networks, it is shown that the ranking depends on the dynamical regime of the disease spreading. A possible mechanism leading to… Expand
Numerical investigation of metrics for epidemic processes on graphs
TLDR
This study develops the epidemic hitting time metric on graphs measuring the expected time an epidemic starting at node a in a fully susceptible network takes to propagate and reach node b, and finds two surprising observations. Expand
Understanding the influence of all nodes in a network
  • G. Lawyer
  • Computer Science, Medicine
  • Scientific reports
  • 2015
TLDR
The expected force, which accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks, can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large. Expand
Using LTI Dynamics to Identify the Influential Nodes in a Network
TLDR
The Node Imposed Response (NiR), a measure which accurately evaluates node spreading power, is proposed which outperforms betweenness, degree, k-shell and h-index centrality in many cases and shows the similar accuracy to dynamics-sensitive centrality. Expand
Dynamic communicability and epidemic spread: a case study on an empirical dynamic contact network
TLDR
It is shown that temporal centrality identifies a distinct set of top-spreaders than centrality based on the time-aggregated binarized contact matrix, so that taken together, the accuracy of capturing top- spreaders improves significantly and the temporal measure does not necessarily outperform less complex measures. Expand
The fastest spreader in SIS epidemics on networks
TLDR
It is shown that the fastest spreader may change with the effective infection rate of a SIS epidemic process, which means that the time-dependent influence of a node is usually strongly coupled to the dynamic process and the underlying network. Expand
Benchmarking Measures of Network Influence
TLDR
It is found that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures. Expand
Power iteration ranking via hybrid diffusion for vital nodes identification
TLDR
Two physical processes with potential complementarity between them are proposed to combine as an elegant integration with the classic eigenvector centrality framework to improve the accuracy of node ranking and are applied to the selection of attack targets in network optimal attack problem. Expand
The dynamic importance of nodes is poorly predicted by static topological features
TLDR
It is concluded that the most dynamically impactful nodes are usually not the most well-connected or central nodes, which implies that the common assumption of topologically central or well- connected nodes being also dynamically important is actually false, and abstracting away the dynamics from a network before analyzing is not advised. Expand
DYNAMIC IMPORTANCE OF NETWORK NODES IS POORLY PREDICTED BY STATIC STRUCTURAL FEATURES
One of the most central questions in network science is: which nodes are most important? Often this question is answered using structural properties such as high connectedness or centrality in theExpand
Influence spreading model used to analyse social networks and detect sub-communities
  • Vesa Kuikka
  • Computer Science, Medicine
  • Computational social networks
  • 2018
TLDR
A dynamic influence spreading model is presented for computing network centrality and betweenness measures, which takes into account different paths between nodes in the network structure and is more realistic compared to classical structural, simulation and random walk models. Expand
...
1
2
3
4
...

References

SHOWING 1-10 OF 57 REFERENCES
Epidemic thresholds in dynamic contact networks
TLDR
It is shown that social mixing fundamentally changes the epidemiological landscape and, consequently, that static network approximations of dynamic networks can be inadequate. Expand
Viral conductance: Quantifying the robustness of networks with respect to spread of epidemics
TLDR
A novel measure, viral conductance (VC), to assess the robustness of complex networks with respect to the spread of SIS epidemics, which incorporates the fraction of infected nodes at steady state for all possible effective infection strengths. Expand
Phase diagram of epidemic spreading — unimodal vs. bimodal probability distributions
Disease spreading on complex networks is studied in SIR model. Simulations on empirical complex networks reveal two specific regimes of disease spreading: local containment and epidemic outbreak. TheExpand
The relationship between human behavior and the process of epidemic spreading in a real social network
On the basis of experimental data on interactions between humans we have investigated the process of epidemic spreading in a social network. We found that the distribution of the number of contactsExpand
Epidemic spreading in scale-free networks.
TLDR
A dynamical model for the spreading of infections on scale-free networks is defined, finding the absence of an epidemic threshold and its associated critical behavior and this new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks. Expand
Superspreading and the effect of individual variation on disease emergence
TLDR
It is shown that contact tracing data from eight directly transmitted diseases shows that the distribution of individual infectiousness around R0 is often highly skewed, and implications for outbreak control are explored, showing that individual-specific control measures outperform population-wide measures. Expand
A contribution to the mathematical theory of epidemics
TLDR
The present communication discussion will be limited to the case in which all members of the community are initially equally susceptible to the disease, and it will be further assumed that complete immunity is conferred by a single infection. Expand
Invasion threshold in heterogeneous metapopulation networks.
TLDR
An explicit expression of the threshold that sets a critical value of the diffusion/mobility rate below, which the epidemic is not able to spread to a macroscopic fraction of subpopulations is provided. Expand
A measure of individual role in collective dynamics
TLDR
It is shown that dynamical influence measures explicitly how strongly a node's dynamical state affects collective behavior, and quantifies how efficiently real systems may be controlled by manipulating a single node. Expand
The role of the airline transportation network in the prediction and predictability of global epidemics
TLDR
A stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data and defines a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. Expand
...
1
2
3
4
5
...