  We consider feature representation learning problem of molecular graphs.
Graph Neural Networks have been widely used in feature representation learning
of molecular graphs. However, most existing methods deal with molecular graphs
individually while neglecting their connections, such as motif-level
relationships. We propose a novel molecular graph representation learning
method by constructing a heterogeneous motif graph to address this issue. In
particular, we build a heterogeneous motif graph that contains motif nodes and
molecular nodes. Each motif node corresponds to a motif extracted from
molecules. Then, we propose a Heterogeneous Motif Graph Neural Network (HM-GNN)
to learn feature representations for each node in the heterogeneous motif
graph. Our heterogeneous motif graph also enables effective multi-task
learning, especially for small molecular datasets. To address the potential
efficiency issue, we propose to use an edge sampler, which can significantly
reduce computational resources usage. The experimental results show that our
model consistently outperforms previous state-of-the-art models. Under
multi-task settings, the promising performances of our methods on combined
datasets shed light on a new learning paradigm for small molecular datasets.
Finally, we show that our model achieves similar performances with
significantly less computational resources by using our edge sampler.
