  Multi-view data containing complementary and consensus information can
facilitate representation learning by exploiting the intact integration of
multi-view features. Because most objects in real world often have underlying
connections, organizing multi-view data as heterogeneous graphs is beneficial
to extracting latent information among different objects. Due to the powerful
capability to gather information of neighborhood nodes, in this paper, we apply
Graph Convolutional Network (GCN) to cope with heterogeneous-graph data
originating from multi-view data, which is still under-explored in the field of
GCN. In order to improve the quality of network topology and alleviate the
interference of noises yielded by graph fusion, some methods undertake sorting
operations before the graph convolution procedure. These GCN-based methods
generally sort and select the most confident neighborhood nodes for each
vertex, such as picking the top-k nodes according to pre-defined confidence
values. Nonetheless, this is problematic due to the non-differentiable sorting
operators and inflexible graph embedding learning, which may result in blocked
gradient computations and undesired performance. To cope with these issues, we
propose a joint framework dubbed Multi-view Graph Convolutional Network with
Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive
graph fusion layer, a graph learning module and a differentiable node selection
schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims
to learn more robust graph fusion through a differentiable neural network. The
effectiveness of the proposed method is verified by rigorous comparisons with
considerable state-of-the-art approaches in terms of multi-view semi-supervised
classification tasks.
