  Graph convolutional network (GCN) is an emerging neural network approach. It
learns new representation of a node by aggregating feature vectors of all
neighbors in the aggregation process without considering whether the neighbors
or features are useful or not. Recent methods have improved solutions by
sampling a fixed size set of neighbors, or assigning different weights to
different neighbors in the aggregation process, but features within a feature
vector are still treated equally in the aggregation process. In this paper, we
introduce a new convolution operation on regular size feature maps constructed
from features of a fixed node bandwidth via sampling to get the first-level
node representation, which is then passed to a standard GCN to learn the
second-level node representation. Experiments show that our method outperforms
competing methods in semi-supervised node classification tasks. Furthermore,
our method opens new doors for exploring new GCN architectures, particularly
deeper GCN models.
