I don't know if you know the time stamp of each post. If not, then one possible model is that for each node $i$ with state $(x_i, y_{i1}, y_{i2}, \ldots, y_{ij})$, where $x_i$ is the number of original post for user $i$ and $y_{ij}$ is the number of posts influenced by other user $j$, define the probability model as follows:

$$P(\mathrm{next\ post\ is\ original\ for\ user\ }i) = \frac{x_i}{x_i+y_i + \alpha}$$ where $y_i = \sum_{j} y_{ij}$.

Similarly, if user $i$ and user $j$ is previously connected, then

$$P(\mathrm{next\ post\ of\ user\ }i\ \mathrm{is\ related\ to\ user\ }j) = \frac{y_{ij}}{x_i+y_i+\alpha}$$

And with probability $\alpha/(x_i+y_i+\alpha)$, user $i$ will react on a post by user $j$, who has not been connected before. In both cases, $\alpha > 0$ is a tunable constant.

This model is similar to Chinese Restaurant Processes. See link text. The intuition is that if a user $i$ has been connected to $j$ for many times, then he/she is likely to have more connections in the future. Similarly, if a user tends to post originally for many times, then he will behave similarly in the future.