  Graph Neural Networks (GNN) show great promise in problems dealing with
graph-structured data. One of the unique points of GNNs is their flexibility to
adapt to multiple problems, which not only leads to wide applicability, but
also poses important challenges when finding the best model or acceleration
technique for a particular problem. An example of such challenges resides in
the fact that the accuracy or effectiveness of a GNN model or acceleration
technique generally depends on the structure of the underlying graph. In this
paper, in an attempt to address the problem of graph-dependent acceleration, we
propose ProGNNosis, a data-driven model that can predict the GNN training time
of a given GNN model running over a graph of arbitrary characteristics by
inspecting the input graph metrics. Such prediction is made based on a
regression that was previously trained offline using a diverse synthetic graph
dataset. In practice, our method allows making informed decisions on which
design to use for a specific problem. In the paper, the methodology to build
ProGNNosis is defined and applied for a specific use case, where it helps to
decide which graph representation is better. Our results show that ProGNNosis
helps achieve an average speedup of 1.22X over randomly selecting a graph
representation in multiple widely used GNN models such as GCN, GIN, GAT, or
GraphSAGE.
