  Graph Neural Networks (GNNs) is an architecture for structural data, and has
been adopted in a mass of tasks and achieved fabulous results, such as link
prediction, node classification, graph classification and so on. Generally, for
a certain node in a given graph, a traditional GNN layer can be regarded as an
aggregation from one-hop neighbors, thus a set of stacked layers are able to
fetch and update node status within multi-hops. For nodes with sparse
connectivity, it is difficult to obtain enough information through a single GNN
layer as not only there are only few nodes directly connected to them but also
can not propagate the high-order neighbor information. However, as the number
of layer increases, the GNN model is prone to over-smooth for nodes with the
dense connectivity, which resulting in the decrease of accuracy. To tackle this
issue, in this thesis, we define a novel framework that allows the normal GNN
model to accommodate more layers. Specifically, a node-degree based gate is
employed to adjust weight of layers dynamically, that try to enhance the
information aggregation ability and reduce the probability of over-smoothing.
Experimental results show that our proposed model can effectively increase the
model depth and perform well on several datasets.
