  Graph Convolutional Networks (GCNs) have been widely studied for compact data
representation and semi-supervised learning tasks. However, existing GCNs
usually use a fixed neighborhood graph which is not guaranteed to be optimal
for semi-supervised learning tasks. In this paper, we first re-interpret graph
convolution operation in GCNs as a composition of feature propagation and
(non-linear) transformation. Based on this observation, we then propose a
unified adaptive neighborhood feature propagation model and derive a novel
Adaptive Neighborhood Graph Propagation Network (ANGPN) for data representation
and semi-supervised learning. The aim of ANGPN is to conduct both graph
construction and graph convolution simultaneously and cooperatively in a
unified formulation and thus can learn an optimal neighborhood graph that best
serves graph convolution for data representation and semi-supervised learning.
One main benefit of ANGPN is that the learned (convolutional) representation
can provide useful weakly supervised information for constructing a better
neighborhood graph which meanwhile facilitates data representation and
learning. Experimental results on four benchmark datasets demonstrate the
effectiveness and benefit of the proposed ANGPN.
