  Machine learning models that can exploit the inherent structure in data have
gained prominence. In particular, there is a surge in deep learning solutions
for graph-structured data, due to its wide-spread applicability in several
fields. Graph attention networks (GAT), a recent addition to the broad class of
feature learning models in graphs, utilizes the attention mechanism to
efficiently learn continuous vector representations for semi-supervised
learning problems. In this paper, we perform a detailed analysis of GAT models,
and present interesting insights into their behavior. In particular, we show
that the models are vulnerable to heterogeneous rogue nodes and hence propose
novel regularization strategies to improve the robustness of GAT models. Using
benchmark datasets, we demonstrate performance improvements on semi-supervised
learning, using the proposed robust variant of GAT.
