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Error And Attack Vulnerability Of Temporal Networks

Contents 1 Attack Types 1.1 Average Node Degree Attack 1.2 Node Persistence Attack 1.3 Temporal Closeness Attack 2 Results for Different Networks 2.1 Erdős–Rényi model 2.2 Scale-free model 3 See also As the number of attacks increases, S decreases with a threshold close to f=0.05, and increases to the same threshold and then decreases back to one. Educ. Elegans is particularly challenging and so far only a small number of synapses have been examined. my review here

In the case that there is no connection from node v to w at time t, we have Svw[t]=0.In a spatio-temporal system, the constituent entities naturally occupy a location in space, For example, in Paris Metro and US Flights we see high correlation among most pairs of strategies, and also observe that these strategies have similar robustness profiles, especially in the case Elegans does not reach full coverage. We adopt a methodology to evaluate the impact of such events on the network connectivity by employing temporal metrics in order to select and remove nodes based on how critical they http://www.ncbi.nlm.nih.gov/pubmed/23005160

Rev. Beams Phys. As an example, transit between stations in a public transport system naturally incurs a time delay while a passenger travels, and the specific delay depends on the speed of the service Robustness of temporal efficiency under systematic attack.

Many studies of the last decade examine how the static network structure affect dynamic systems on the network. PB preferentially attacks nodes through which many geodesic paths flow, whereas BE prioritizes nodes supporting temporally efficient paths. We note that, unlike the London Metro dataset, the flight times are from reported data, rather than a priori timetables. In the case of the two transport networks, we see the formation of spatio-temporal paths during normal operating hours, contrasting with little or no growth during early morning.

Consider a set of deactivated nodes D⊆V . Interestingly, the threshold at which systematic attack eliminates the giant component in US Flights (figure 6d) is much lower; specifically, this occurs at f=0.16 for US Flights and at f=0.45 for The propagation speeds in these networks are also heterogeneous, with the amount of diversity depending on the particular system; for example, longer track segments in London and longer flight paths in https://www.researchgate.net/publication/257172525_Error_and_attack_vulnerability_of_temporal_networks Please try the request again.

Summary of the six spatio-temporal systems explored in this paper: timetabled London Underground transits (London Metro), Paris Metro transits (Paris Metro), New York City Subway transits (New York Metro), US domestic The work relies on the widely accepted temporal network analysis and network robustness. More formally, the reachability set at timestep ti is expressed as K[ti]=K[ti−1]∪{w | ∃v s.t. Pvw[ti]≥Dvw[ti]}.2.4In the preceding equation, Pvw[ti]≥Dvw[ti] represents the case that sufficient time has elapsed for propagation to complete, expressed in terms The advantage compared to common static network approaches is the ability to design more accurate models in order to explain and predict large-scale dynamic phenomena (such as, e.g., epidemic outbreaks and

For the given choice of τ, this quantity represents the minimum direct propagation duration that exists in any of the system's snapshots. http://rsos.royalsocietypublishing.org/content/3/6/160196 Specifically, we apply the temporal betweenness centrality introduced in [50], which for convenience we refer to as PTPB (pure temporal path betweenness). A progress element represents the state of partial propagation between the two nodes at the end of a timestep. The APS Physics logo and Physics logo are trademarks of the American Physical Society.

Three urban transport networks and the US flights network. http://axishost.net/error-and/error-and-attack-tolerance-of-complex-networks-bibtex.php In our model, Svw[t] is a non-negative scalar representing the speed of physical propagation from v to w in the corresponding time interval. We find that spatio-temporal distances tend to take longer routes through the network than purely spatial paths in New York. For many types of spatially embedded system, this assumption ignores the influence that space has in constraining the structure of the network.

Each node is a US airport. Erdős–Rényi model[edit] Main article: Erdős–Rényi model In the ER model, the network generated is homogeneous, meaning each node has the same number of links. Furthermore, critical nodes play different roles in terms of their topological, temporal and spatial utility, and therefore systematic attack strategies can differ in the damage they cause to the network.2. get redirected here Rev.

was a student at University of Cambridge.†[email protected][email protected] Text (Subscription Required) Click to ExpandReferences (Subscription Required) IssueVol. 85, Iss. 6 — June 2012Reuse & PermissionsAccess OptionsBuy Article »Get access through a U.S. Rev. We make the simplifying assumption that multiple nodes in the network cannot occupy the same location.

In other words, these temporal paths are not constrained by distance and propagation speed. (Equivalently, we can regard this as the case where all propagation speeds are infinite.) We show the

To study the information sharing potential of this network using our spatio-temporal framework, we construct the patterns of communication between students via their SMS and phone logs. The StudentLife experiment [69] used continuous smartphone monitoring to follow a cohort of students at Dartmouth College over one academic semester. These datasets and visualizations are further described in this paper's electronic supplementary material.Authors' contributionsM.J.W. The process is represented by two time-evolving structures: a reachability set K[t] and an N-by-N progress matrix P[t].

Please review our privacy policy. Here we explore whether a centrality measure based on temporally shortest paths has the same effect as PB and BE. Here we can see that source node A reaches B and C in at most three subsequent timesteps. http://axishost.net/error-and/error-and-attack-tolerance-of-complex-networks-albert.php At this point (i.e.

Finally, as a diagnostic of the reciprocity in the whole time-varying network, we define the weight reciprocity as the average weighted reciprocity ρ¯=1T∑i=1Tρ[ti].A 5Weight reciprocity is normalized between 0 and 1. Shortest spatial paths follow minimum spatial length. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the Specifically, we observe correlation coefficients 0.732 for London Metro, 0.788 for Paris Metro, 0.613 for New York Metro, 0.961 for US Flights and 0.613 for C.

In figure 4a, we see that the network becomes highly fragmented after very few failures, and the giant component is effectively eliminated (filling less than 5% of the network) at failure Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We denote the temporal efficiency of a network with deactivated nodes D by Eλ(D). We select the timetable of December 2015 and set the observation start time to Monday at 00.00.New York Subway (New York Metro).

Owing to the non-transitive and non-symmetric nature of spatio-temporal paths, in practice, we must use the affine graph method to compute giant component sizes (see [53] for details), which is the Attack strategiesWe summarize our five chosen attack strategies as follows. Specifically, full propagation from A to B is able to occur in one timestep, arriving at timestep t2. Robustness of the giant strongly connected component when each system is subject to five systematic attack strategies: TC, PB, BE, ID and OD.

Here we introduce the measures we use to evaluate the topological, temporal and spatial vulnerability of a spatio-temporal network. Download figureOpen in new tabDownload powerpointFigure 6. Elegans connectome, Manuel Zimmer for advice on synaptic delay and Luca Rossi for fruitful discussions. N p G ( i ; t 1 , t n ) = 1 n ∑ j = 1 n δ t j ( i ) {\displaystyle Np_{G}(i;t_{1},t_{n})=\textstyle {\frac {1}{n}}\sum _{j=1}^{n}{\delta

For instance, it indicates whether nodes that appear to be with high degrees have also high betweenness.Reuse & PermissionsFigure 14Histograms of Markov model nodal properties. Our observation window covers six weeks from Monday 8 April 2013.Our framework offers an alternative approximation of passenger transit (i.e. Temporal granularity τ=1 s.