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+"""
+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: