1 files changed, 46 insertions, 0 deletions
diff --git a/nips-2016-988.txt b/nips-2016-988.txt
new file mode 100644
index 0000000..40f4513
--- /dev/null
+++ b/nips-2016-988.txt
@@ -0,0 +1,46 @@
+Summary
+=======
+
+This paper studies the problem of network inference problem for the RDS
+(Respondent Driven Sampling) diffusion process. This random diffusion process
+models a standard procedure used in epidemiology studies. Under this model, the
+authors formulate the network reconstruction process (finding the unknown graph
+over which the diffusion took place) as a MAP (maximum a posteriori)
+optimization problem. The contributions are:
+
+* showing that the objective function is log-submodular
+
+* giving a "variational inference" algorithm: the objective function is
+approximated by linear lower and upper bounds and solving for those upper and
+lower bounds provides an approximate solution to the problem
+
+* experiments showing the validity of the approach
+
+Comments
+========
+
+The problem of network reconstruction is important and hadn't been studied from
+this perspective in the context of Respondent Driven Sampling. While the
+experiments show that the suggested approach works well in practice, I find the
+paper to be lacking a conceptual/technical contribution:
+
+* it was already shown that the log likelihood was submodular in [8] for the
+closely related continuous-time independent cascade model. Even though the
+diffusion process here is slightly different, this result can hardly be
+considered novel.  
+
+* the suggested approach (approximating the objective function with linear
+lower and upper bounds) was alread suggested in [5], including the specific
+choice of the lower and upper additive approximations
+
+* the formulas from [5] are barely instantiated to the specific objective
+function at hand here. It could have been interesting to show how the general
+framework of [5] leads to a simple algorithm for this specific problem, but no
+algorithm is provided and very little is said about how the computation should
+be performed in practice. For example the paragraph starting at line 252 is
+very vague.
+
+* no theoretical guarantees are provided for the approximation obtained by the
+suggested approach. Some guarantees were obtained in [5] under a curvature
+assumption. It could have been interesting to show which form these assumptions
+take in this specific context.