  For learning graph representations, not all detailed structures within a
graph are relevant to the given graph tasks. Task-relevant structures can be
$localized$ or $sparse$ which are only involved in subgraphs or characterized
by the interactions of subgraphs (a hierarchical perspective). A graph neural
network should be able to efficiently extract task-relevant structures and be
invariant to irrelevant parts, which is challenging for general message passing
GNNs. In this work, we propose to learn graph representations from a sequence
of subgraphs of the original graph to better capture task-relevant
substructures or hierarchical structures and skip $noisy$ parts. To this end,
we design soft-mask GNN layer to extract desired subgraphs through the mask
mechanism. The soft-mask is defined in a continuous space to maintain the
differentiability and characterize the weights of different parts. Compared
with existing subgraph or hierarchical representation learning methods and
graph pooling operations, the soft-mask GNN layer is not limited by the fixed
sample or drop ratio, and therefore is more flexible to extract subgraphs with
arbitrary sizes. Extensive experiments on public graph benchmarks show that
soft-mask mechanism brings performance improvements. And it also provides
interpretability where visualizing the values of masks in each layer allows us
to have an insight into the structures learned by the model.
