  The decoupled Graph Convolutional Network (GCN), a recent development of GCN
that decouples the neighborhood aggregation and feature transformation in each
convolutional layer, has shown promising performance for graph representation
learning. Existing decoupled GCNs first utilize a simple neural network (e.g.,
MLP) to learn the hidden features of the nodes, then propagate the learned
features on the graph with fixed steps to aggregate the information of
multi-hop neighborhoods. Despite effectiveness, the aggregation operation,
which requires the whole adjacency matrix as the input, is involved in the
model training, causing high training cost that hinders its potential on larger
graphs. On the other hand, due to the independence of node attributes as the
input, the neural networks used in decoupled GCNs are very simple, and advanced
techniques cannot be applied to the modeling. To this end, we further liberate
the aggregation operation from the decoupled GCN and propose a new paradigm of
GCN, termed Neighborhood Convolutional Network (NCN), that utilizes the
neighborhood aggregation result as the input, followed by a special
convolutional neural network tailored for extracting expressive node
representations from the aggregation input. In this way, the model could
inherit the merit of decoupled GCN for aggregating neighborhood information, at
the same time, develop much more powerful feature learning modules. A training
strategy called mask training is incorporated to further boost the model
performance. Extensive results demonstrate the effectiveness of our model for
the node classification task on diverse homophilic graphs and heterophilic
graphs.
