  Graph convolutional network (GCN) is a powerful model studied broadly in
various graph structural data learning tasks. However, to mitigate the
over-smoothing phenomenon, and deal with heterogeneous graph structural data,
the design of GCN model remains a crucial issue to be investigated. In this
paper, we propose a novel GCN called SStaGCN (Simplified stacking based GCN) by
utilizing the ideas of stacking and aggregation, which is an adaptive general
framework for tackling heterogeneous graph data. Specifically, we first use the
base models of stacking to extract the node features of a graph. Subsequently,
aggregation methods such as mean, attention and voting techniques are employed
to further enhance the ability of node features extraction. Thereafter, the
node features are considered as inputs and fed into vanilla GCN model.
Furthermore, theoretical generalization bound analysis of the proposed model is
explicitly given. Extensive experiments on $3$ public citation networks and
another $3$ heterogeneous tabular data demonstrate the effectiveness and
efficiency of the proposed approach over state-of-the-art GCNs. Notably, the
proposed SStaGCN can efficiently mitigate the over-smoothing problem of GCN.
