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Figure 4 shows a partial visual representation of the diffusion graph corresponding to the French candidates.
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Open image in new window Fig. 3 Diffusion graphs of CeO2 nanoparticles in water for temperatures 300 325KK D = 1 6 d r i ( t ) - r i ( 0 ) 2 / d t (2).
In conclusion, this metric is not a good one to capture hierarchical structure for social diffusion graphs, and the Gini coefficient for in-degree inequality represents a more reliable measure.
In other words, the in-degree centralization is not a good metric to capture hierarchical structure for social diffusion graphs, and the Gini coefficient for in-degree inequality represents a more reliable measure of the hierarchical structure of a network.
We rigorously establish the convergence of the numerical method to the related diffusion on graph, identifying the appropriate choice of discretization parameters.
Fig. 2 HTO through-diffusion (upper graph) and flux (lower graph) curves for cell Trac2, run 2 (circles) without pore space clogging (or reduction), and cells Prec2 and Prec3 (open and full triangles, respectively) while reducing porosity by celestite precipitation.
We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph.
We consider multiscale SDEs with potentially multiple attractors that behave as diffusions on graphs as the stiffness parameter goes to its limit.
Another technique for incorporating indirect neighbors is graph diffusion, an idea derived from the study of diffusion in physical systems.
Graph diffusion can be seen as network smoothing, as for increasing levels of diffusion β, the edge weights in the graph become more and more similar.
Graph diffusion kernels are solutions to the steady-state density distribution for continuous-time random walk or diffusive process on a graph with sources and sinks [ 8].
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