  Graph Convolutional Networks (GCNs) have shown very powerful for graph data
representation and learning tasks. Existing GCNs usually conduct feature
aggregation on a fixed neighborhood graph in which each node computes its
representation by aggregating the feature representations of all its neighbors
which is biased by its own representation. However, this fixed aggregation
strategy is not guaranteed to be optimal for GCN based graph learning and also
can be affected by some graph structure noises, such as incorrect or undesired
edge connections. To address these issues, we propose a novel Graph mask
Convolutional Network (GmCN) in which nodes can adaptively select the optimal
neighbors in their feature aggregation to better serve GCN learning. GmCN can
be theoretically interpreted by a regularization framework, based on which we
derive a simple update algorithm to determine the optimal mask adaptively in
GmCN training process. Experiments on several datasets validate the
effectiveness of GmCN.
