Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years-mostly on tasks involving node-level predictions. The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or side-effect prediction for a drug), however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction. Most prior work attempts to predict these graph-level properties while considering only one graph at a time-not allowing the learner to directly leverage structural similarities and motifs across graphs. Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them. We first show that such a setup provides natural benefits on a pairwise graph classification task (drug-drug interaction prediction), and then expand to more generic graph regression and graph classification setups: enhancing predictions over QM9, D&D and PROTEINS, standard molecular prediction benchmarks. Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs.
In this paper, the authors proposed a method to extend graph-based learning with a co-attentional layer. Feeding graphs pairwisely into the model allows nodes to easily exchange information with nodes in other graphs as well as within the graph. This method outperforms other previous ones on a pairwise graph classification task (drug-drug interaction prediction).
This model is generalized from Neural Message Passing for Quantum Chemistry (Justin Gilmer et al.) and Graph Attention Networks (Petar Velickovic et al.), but most ideas are directly from the two previous papers. Combining the two methods do provide insights into understanding the interactions between graphs and get really good results on DDI prediction, but the novelty is limited.
Questions:
1 Are atoms encoded as only atom numbers, charges and connected hydrogen atoms? Because some atoms might have much larger atom numbers than others, e.g. carbon (6) and sulfur (16), will there be some scale problems? Will one-hot encoding of atom type help (like in Neural Message Passing for Quantum Chemistry)?
2 According to the paper, bond types will be encoded as e_{ij}. But in molecules, bond type is way more complex than only single/double/triple bonds, especially for drug molecules which are enriched for aromatic systems. For example, bonds in benzene or pyridine rings are between single and double (also not necessarily 3/2). Are there other possible methods to encode graph edges?
3 In result table 2 of Section 4 (quantum chemistry), I didn’t see a principle of choosing K value and choosing neighbors because different properties reaches the lowest MAE at different K values. This might cause some confusion in real application. Moreover, the authors should compare the performance with previous methods.