diffusion with (a)social learning in a dark network with constant death rate [closed] - igraph

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I am trying to combine the target and random attacks model in Albert, Jeong, and Barabási (2000) with gossip diffusion on a given organized crime network.
In my theoretical framework, gangsters gossip about the death of other workmen elsewhere in their network neighborhood. Hence, the gangsters learn the condolence news as their camarades die out sequentially every period either through social ties (social learning) or through exogenous sources (asocial learning). What’s unfortunate, some gangsters may not survive another day to hear the next death news.
For the task, I am using the Dai Shizuka’s code for diffusion through social learning (via social ties) and through asocial learning (exogenous to the network sources) in social networks as illustrated in the article about social learning of free-living animals by Franz and Nunn (2009). I assume gangsters learn about the condolence gossip either through their personal networks or through an external source.
In the first scenario, I must kill 13 historically well-known gangsters from a list published online. In the second scenario, I must remove the 30 most central gangsters, whereas in the last scenario, I have to randomly delete 30 gangsters from the mafia network. At the end, I compare the s-curves for social learning and asocial learning for each scenario. Obviously, the innovation is the inclusion of attrition to diffusion models on an original setting, the Italian mafia network in the U.S.
My question is how to create the appropriate temporal network object which works with Dai Shizuka’s code.The code isn't designed for temporal longitudinal networks.
References
Albert, R., Jeong, H. and Barabási, A.L., 2000. Error and attack tolerance of complex networks. nature, 406(6794), pp.378-382.
Dai Shizuka, 2023. “8. Intro to diffusion on networks.” Available at: https://dshizuka.github.io/networkanalysis/08_diffusion.html. Accessed: 8 February, 2023.
Franz, M. and Nunn, C.L., 2009. Network-based diffusion analysis: a new method for detecting social learning. Proceedings of the Royal Society B: Biological Sciences, 276(1663), pp.1829-1836.
I’ve also tried modeling the process with the alternative networkDynamic and netdiffuseR packages, yet was rather dissatisfied with the final presentation product and the way the diffusion plots were presented in netdiffuseR. I also report some issues with running the toy diffusion example in networkDynamic, p. 45.
Because there are too many diffusion models for various social phenomena in the social networks literature, we really must know the specificities of the various diffusion models and the network mechanisms behind them. For instance, is the process of gossip diffusion like the process of STDs diffusion? Why isn't that the case?