We propose FastGAT , a method
FastGAT can dramatically reduce <*> the computational time and memory requirements ,
We propose FastGAT , a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph .
We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks , FastGAT can dramatically reduce ( up to 10x ) the computational time and memory requirements , allowing the usage of attention based GNNs on large graphs .