  Graph convolutional neural networks (GCN) have been the model of choice for
graph representation learning, which is mainly due to the effective design of
graph convolution that computes the representation of a node by aggregating
those of its neighbors. However, existing GCN variants commonly use 1-D graph
convolution that solely operates on the object link graph without exploring
informative relational information among object attributes. This significantly
limits their modeling capability and may lead to inferior performance on noisy
and sparse real-world networks. In this paper, we explore 2-D graph convolution
to jointly model object links and attribute relations for graph representation
learning. Specifically, we propose a computationally efficient dimensionwise
separable 2-D graph convolution (DSGC) for filtering node features.
Theoretically, we show that DSGC can reduce intra-class variance of node
features on both the object dimension and the attribute dimension to learn more
effective representations. Empirically, we demonstrate that by modeling
attribute relations, DSGC achieves significant performance gain over
state-of-the-art methods for node classification and clustering on a variety of
real-world networks. The source code for reproducing the experimental results
is available at https://github.com/liqimai/DSGC.
