Information spreading is a typical use case in the analysis of temporal (dynamic) social networks.
On this second assignment, we are going to develop a data-driven epidemic diffusion process. In particular, we are going to simulate Susceptible-Infected process (SI model). On this model, a rumor is going to be spread through the Mathoverflow social network. A random user, the first infected user, might spread the rumor to his neighbors depending on a certain probability, the next time they interact with each other. So, if the neighbor is infected, he will be also a possible spreader to his neighbors at the moment he interacts with them, and so on. Once an user is infected, he is not susceptible any more so an infected user must not be considered to be infected again.
You have to develop the neccesary R functions to simulate this process with other some requirements:
* Since the initial node of the spreading is random, you have to choose this seed node within the list of active (with some interactions) nodes in the first week of the data.
* Your software must be parametrized so, at least, it should have:
+ The interaction dataset, with this layout: origin, destination, timestamp
+ The probability of infection
+ The number of simulations (that is, number of times this process is executed)
Think of the problem we have just defined. The idea is to create some R functions to simulate information spreading in our social network, satisfying the previous description of the process. Before programming, you have to design and describe in this sections the steps you need to develope later to simulate the diffusion process. Please, read the full statement of this assignment in order to acquire a full view of the requirements and after this, you will be able to design it properly.
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