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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. Toaddress all these problems simultaneously, we propose a novel graph neuralnetwork FDGATII, inspired by attention mechanism's ability to focus onselective information supplemented with two feature preserving mechanisms.FDGATII combines Initial Residuals and Identity Mapping with the moreexpressive dynamic self-attention to handle noise prevalent from theneighbourhoods 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 approachhas been extensively evaluated on 7 datasets. We show that FDGATII outperformsGAT and GCN based benchmarks in accuracy and performance on fully supervisedtasks, obtaining state-of-the-art results on Chameleon and Cornell datasetswith zero domain-specific graph pre-processing, and demonstrate its versatilityand fairness.

FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping