In this subsection, we evaluate the performance of information dissemination with relevant factors of the proposed model.

Effects of Spreading Parameter and Immune Parameter

We evaluate the effects of spreading parameter and immune parameter on the information dissemination. Here, R(t) is used as the performance metric. In order to study the impact of spreading parameter and immune parameter, we ignore the selfimmune parameter such that all recovered nodes can have the information under this situation. Here, the spreading parameter в increases from 0 to 1, and four values of 8 are randomly chosen for comparison, which are 0.1,0.3, 0.7, and 1. Other settings are the same as those in Fig. 2.5. Figure 2.6 shows the final number of the recovered nodes R versus different values of в and 8. FromFig. 2.6, it is observed that the information can be still transmitted if в is very small. For example, when в = 0.1 and 8 = 0.3, the number of the recovered node R is close to 200. Another phenomenon is that the final R increases with the increase of the value of в, while the increasing speed decreases gradually. Moreover, the larger immune parameter has smaller value of the final R. A large spreading parameter в can promote information dissemination, while immune parameter 8 has a negative effect on information dissemination. Moreover, when 8 is smaller, the network is more robust to в. For example, when 8 = 0.1, the value of final R remains stable when в > 0.6.

Fig. 2.6 The final number of recovered nodes R with different spreading parameter в and different immune parameter &

Fig. 2.7 Time evolutions of the number of spreading nodes S(t) with different initial spreading nodes