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We revisit recent spectral GNN approaches to semi-supervised nodeclassification (SSNC). We posit that many of the current GNN architectures maybe over-engineered. Instead, simpler, traditional methods from nonparametricestimation, applied in the spectral domain, could replace many deep-learninginspired GNN designs. These conventional techniques appear to be well suitedfor a variety of graph types reaching state-of-the-art performance on many ofthe common SSNC benchmarks. Additionally, we show that recent performanceimprovements in GNN approaches may be partially attributed to shifts inevaluation conventions. Lastly, an ablative study is conducted on the varioushyperparameters associated with GNN spectral filtering techniques. Codeavailable at: https://github.com/lucianoAvinas/lowrank-gnn-kernels

Simplifying GNN Performance with Low Rank Kernel Models