  While Graph Neural Networks have gained popularity in multiple domains,
graph-structured input remains a major challenge due to (a) over-smoothing, (b)
noisy neighbours (heterophily), and (c) the suspended animation problem. To
address all these problems simultaneously, we propose a novel graph neural
network FDGATII, inspired by attention mechanism's ability to focus on
selective information supplemented with two feature preserving mechanisms.
FDGATII combines Initial Residuals and Identity Mapping with the more
expressive dynamic self-attention to handle noise prevalent from the
neighbourhoods in heterophilic data sets. By using sparse dynamic attention,
FDGATII is inherently parallelizable in design, whist efficient in operation;
thus theoretically able to scale to arbitrary graphs with ease. Our approach
has been extensively evaluated on 7 datasets. We show that FDGATII outperforms
GAT and GCN based benchmarks in accuracy and performance on fully supervised
tasks, obtaining state-of-the-art results on Chameleon and Cornell datasets
with zero domain-specific graph pre-processing, and demonstrate its versatility
and fairness.
