We provide a model for diffusion of interests in Social Networks (SNs).
Of special note is the diffusion of interests from overtly political and strategic analyses of women in society and culture, to exploration of the variant forms that gender can assume, a shift that also distinguishes aspects of the transition from feminism to postfeminism.
In order to simulate the diffusion problems of interest, high-quality atomic mobilities are indispensable.
Measuring the time it takes to fully convert the indicator particle enables the determination of the diffusion coefficient of interest.
A key aspect of the proposed approach to early warning analysis is determining which characteristics of the social diffusion event of interest, if any, possess exploitable predictive power.
Given: social diffusion process of interest is ∑ S − HDS,diff, CSS = X0 × P0, SSI = {Xs 1, Xs 2} and minimum acceptable level of variation = δ.
We are now in a position to present an early warning method which is capable of accurately predicting, very early in the lifecycle of a diffusion process of interest, whether or not the process will propagate widely.
Prediction accuracy in all tests is estimated using standard N-fold cross-validation, in which the set of diffusion events of interest is randomly partitioned into N subsets of equal size, and the A-EDT algorithm is successively "trained" on N−1 of the subsets and "tested" on the held-out subset in such a way that each of the N subsets is used as the test set exactly once.
The diffusion of the interest rates is often approximated with a tree, in which the scenario-dependent fair price of any security is calculated as the present value of the risk-neutral expectation by backward induction.
In these diffusion maps, regions of interest were placed onto the cortex (1), the outer stripe of the outer medulla (2), the inner stripe of the outer medulla (3) and the inner medulla (4) as exemplarily demonstrated in a.
