The crux of molecular property prediction is to generate meaningful
representations of the molecules. One promising route is to exploit the
molecular graph structure through Graph Neural Networks (GNNs). It is well
known that both atoms and bonds significantly affect the chemical properties of
a molecule, so an expressive model shall be able to exploit both node (atom)
and edge (bond) information simultaneously. Guided by this observation, we
present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing
architecture to enable more accurate predictions of molecular properties. In
MV-GNN, we introduce a shared self-attentive readout component and disagreement
loss to stabilize the training process. This readout component also renders the
whole architecture interpretable. We further boost the expressive power of
MV-GNN by proposing a cross-dependent message passing scheme that enhances
information communication of the two views, which results in the MV-GNN^cross
variant. Lastly, we theoretically justify the expressiveness of the two
proposed models in terms of distinguishing non-isomorphism graphs. Extensive
experiments demonstrate that MV-GNN models achieve remarkably superior
performance over the state-of-the-art models on a variety of challenging
benchmarks. Meanwhile, visualization results of the node importance are
consistent with prior knowledge, which confirms the interpretability power of
MV-GNN models.

使用多视图神经网络和交叉依赖的消息传递机制，以预测分子性质为目的，构建了一种能够同时利用节点（原子）和边（化学键）信息的表达力强的模型，并在多个基准测试中表现出优异性能。

多视图图神经网络用于分子性质预测

Multi-View Graph Neural Networks for Molecular Property Prediction

Knowledge graphs are structured representations of real world facts. However,
they typically contain only a small subset of all possible facts. Link
prediction is a task of inferring missing facts based on existing ones. We
propose TuckER, a relatively straightforward but powerful linear model based on
Tucker decomposition of the binary tensor representation of knowledge graph
triples. TuckER outperforms previous state-of-the-art models across standard
link prediction datasets, acting as a strong baseline for more elaborate
models. We show that TuckER is a fully expressive model, derive sufficient
bounds on its embedding dimensionalities and demonstrate that several
previously introduced linear models can be viewed as special cases of TuckER.

TuckER 是一种基于 Tucker 分解的二元张量表示的线性模型，在标准的链接预测数据集上表现优于其他最先进的模型，并作为更复杂模型的强有力基准，是一种完全表达模型，可以作为先前引入的一些线性模型的特例来看待。