Review for NeurIPS paper: Factor Graph Neural Networks

Weaknesses: The proposed architecture is not particularly novel and experiments can be improved. While the theoretical analysis is quite interesting, it is not significant enough to bypass the aforementioned issues (e.g., the analysis mainly relies on the Lemma 1 proposed by Kohli et al.). While the proposed factor graph neural network (FGNN) is guaranteed to express a family of higher-order interactions, in the end, FGNN is a member of MPNN applied to heterogeneous graph with two types of vertices (random variable and factor). I also think the considered experiments are limited since they only consider the case where (1) training and evaluation are done on the same graph and (2) factors are easily expressed as a representation of fixed dimension. In other words, the considered experiments are not very convincing for showing that the proposed FGNN works across general graphical models.