  Relational data present in real world graph representations demands for tools
capable to study it accurately. In this regard Graph Neural Network (GNN) is a
powerful tool, wherein various models for it have also been developed over the
past decade. Recently, there has been a significant push towards creating
accelerators that speed up the inference and training process of GNNs. These
accelerators, however, do not delve into the impact of their dataflows on the
overall data movement and, hence, on the communication requirements. In this
paper, we formulate analytical models that capture the amount of data movement
in the most recent GNN accelerator frameworks. Specifically, the proposed
models capture the dataflows and hardware setup of these accelerator designs
and expose their scalability characteristics for a set of hardware, GNN model
and input graph parameters. Additionally, the proposed approach provides means
for the comparative analysis of the vastly different GNN accelerators.
