Simplifying GNN Performance with Low Rank Kernel Models
Luciano Vinas Arash A. Amini
We revisit recent spectral GNN approaches to semi-supervised node
classification (SSNC). We posit that many of the current GNN architectures may
be over-engineered. Instead, simpler, traditional methods from nonparametric
estimation, applied in the spectral domain, could replace many deep-learning
inspired GNN designs. These conventional techniques appear to be well suited
for a variety of graph types reaching state-of-the-art performance on many of
the common SSNC benchmarks. Additionally, we show that recent performance
improvements in GNN approaches may be partially attributed to shifts in
evaluation conventions. Lastly, an ablative study is conducted on the various
hyperparameters associated with GNN spectral filtering techniques. Code
available at: https://github.com/lucianoAvinas/lowrank-gnn-kernels