  We present an attention mechanism inspired from definition of screened
Coulomb potential. This attention mechanism was used to interpret the Graph
Attention (GAT) model layers and training dataset by using a flexible and
scalable framework (CoulGAT) developed for this purpose. Using CoulGAT, a
forest of plain and resnet models were trained and characterized using this
attention mechanism against CHAMPS dataset. The learnable variables of the
attention mechanism are used to extract node-node and node-feature interactions
to define an empirical standard model for the graph structure and hidden layer.
This representation of graph and hidden layers can be used as a tool to compare
different models, optimize hidden layers and extract a compact definition of
graph structure of the dataset.
