Mining from graph-structured data is an integral component of graph data management. A
recent trending technique, graph convolutional network (GCN), has gained momentum in
the graph mining field, and plays an essential part in numerous graph-related tasks.
Although the emerging GCN optimization techniques bring improvements to specific
scenarios, they perform diversely in different applications and introduce many trial-and-error
costs for practitioners. Moreover, existing GCN models often suffer from oversmoothing …