Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science. Current lines of GNNs developed for molecular analysis, however, do not fit well on the training set, and their performance does not scale well with the complexity of the network. In this paper, we propose an auxiliary module to be attached to a GNN that can boost the representation power of the model without hindering the original GNN architecture. Our auxiliary module can improve the representation power and the generalization ability of a wide variety of GNNs, including those that are used commonly in biochemical applications.
= Summary
A Graph Neural Network extension integrating a global supernode explicitly into the message passing process. Concretely, message passing along the graph edges is alternated with message passing to/from a fresh super-node. Experiments show that this improves results of a number of common GNN architectures on four datasets.
= Strong/Weak Points
+ Simple but useful extension of the existing super-node idea
+ Experiments on a number of datasets and baseline GNN architectures, providing ample experimental evidence of the usefulness of the method.
- Writing is overcomplicated and uses a lot of jargon ("transmitter unit", "warp gate", "intermodule hyperspace"). I found the text entirely impenetrable and instead simply focused on Fig. 3 + the actual equations.
= Recommendation
This is a nice contribution of minor novelty, with empirical evidence of its usefulness. I believe the paper should be accepted to a large conference such as ICLR.
= Minor Comments
- Fig. 3: Inconsistent "intra-module" (top) vs. "intra module" (bottom) 
- Concurrent work in https://openreview.net/forum?id=B1lnbRNtwr discusses a "sandwich" model which alternates graph message passing with (essentially) a Transformer layer applied to all nodes. This idea seems related (in that it alternates local and global information exchange).