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| -rw-r--r-- | paper/rebuttal.txt | 66 |
1 files changed, 44 insertions, 22 deletions
diff --git a/paper/rebuttal.txt b/paper/rebuttal.txt index 03e7e5e..2b7d32b 100644 --- a/paper/rebuttal.txt +++ b/paper/rebuttal.txt @@ -1,6 +1,6 @@ """ 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 +[...]. 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. """ @@ -25,9 +25,8 @@ 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? +"""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 @@ -40,36 +39,59 @@ 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. +Running time is not discussed here. """ -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. +ANS: This is a valid point. The MLE algorithm from Netrap.-Sangh. 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. +78, 93, 362. The authors regret not to have cited Du et al. 2012 and their work +should be included in the related work section along with other work considering +the estimation of influence in networks. It can be mentioned that Daneshmand et +al adopt the same model as Gomez-R et al '10 and Abrahao et al. '13. The +phrasing can be changed from "Graph Inference" to "Network Inference" with the +requested citations. +""" +in a continuous-time model there are not necessarily steps and property 1 is not +necessarily satisfied +""" +ANS: This is a good point and the distinction can be made. + + +""" +the unrealistic assumption of one time infection chance. +"" +ANS: it is true that the standard discrete-time independent cascade model +studied by Netrapalli-Sanghavi assumes one time infection chance but this +restriction is not made at the Generalized Linear Cascade level and is specific +only to the example of section 2.2.1 + + +""" +The authors need to show at least one common metric for all types for graphs +"" +ANS: this is an interesting point: A graph plotting the same metric for all +considered networks can replace one of the 6 figures. """ 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. +processes is less interesting and easier than the continuos version. """ 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: +immediately clear that discrete-time processes cannot approximate some +continuous time processes efficiently. For example, we can discretize the +continuous time process with exponential transmission likelihood by considering +intervals of time of length dt, binning infections to these intervals, and +considering that nodes remain infected until the final observation time. By +exploiting the memoryless property of the exponential distribution, we recover +its discrete-time analog: the geometric distribution for dt<<1. The problem is +still decomposable and fits into the Generalized Linear Cascade model framework. |