""" multiple sources don't make much of a difference in their model, because cascades only spread over a constant distance before dying out. So if two cascades originate at sources that are more than a constant distance away from each other, it's the same as two consecutive, independent cascades. """ ANS: This is an interesting point. However, in the problem we study the graph is unknown to us. Suppose that two cascades start at the same time at two very different points in the graph. Despite the fact that the infected nodes from each cascade will not overlap, we cannot in practice attribute an infected node to either cascade because this information is hidden to us. """In the independent cascade model, nodes have one chance to infect their neighbors. However, the definition in section 2.2.1. seems to allow for multiple attempts, since at any given time t+1, the probability depends on \theta_j X_t """ ANS: As the reviewer correctly points out, the standard ICC model does not allow for multiple infection attempts over time. The definition of section 2.2.1 also prohibits multiple attempts by considering that nodes stay active for only one time step, defining X_t as the set of nodes active at the previous time step only, and saying that only nodes which have not been infected before are susceptible to be infected. """While this is more theory work, the experiment section does not show any theoretical bound. For example, what would be the guaranteed/expected performance given some number of cascades? """ ANS: This is an interesting point. For the experiment section, we could calculate the theoretical guarantees for the synthetic graphs and observe whether or not the theoretical bounds are pessimistic in practice. """Where is the explanation about Figure 1(f)? What is p_init? """ ANS: This is a typo. It should read "n" the number of cascades. """ Running time is not discussed here. It may not be a big problem as the problem can be decomposed into node-local inference, which can be computed in parallel. Still, it is important to distinguish the work from NS by presenting a running time analysis. """ ANS: This is an interesting point. The MLE algorithm from NS has similar running time to the penalized MLE algorithm. Their greedy algorithm runs considerably faster at the price of a slower convergence rate in practice. A precise comparison of running times can be be included. """ Citations/Related work remarks """ ANS: the corresponding requested citations can be included on lines 42, 68, 75, 78, 93, 362. The authors regret not to have cited Du et al. 2012 and this can be corrected. In the related work section, it can be mentioned that Daneshmand et al adopt the same model as GR et al '10 and Abrahao et al. '13. The phrasing can be changed from "Graph Inference" to "Network Inference" with the requested citations. """ the inference in discrete time, one-time-susceptible contagion processes is less interesting and easier than the continuos version. In fact, the the method proposed cannot be adapted to the latter, as this removes the ability to decompose the problem. """ ANS: This is a valid point. Some things to note are: the generalized cascade model class is sufficiently flexible to include multiple-time-susceptible contagion processes (such as the linear voter model). Furthermore, it is not immediately clear that discrete-time processes cannot approximate continuous time processes efficiently. Consider the following: