Node Classification

Node classification — assigning labels to vertices in a graph using both node features and neighborhood structure — is the flagship task for Graph Neural Networks. GCN (Kipf & Welling, 2017) established the Cora/Citeseer/PubMed benchmark trinity, but these datasets are tiny by modern standards and results have saturated well above 85% accuracy. The field has moved toward large-scale heterogeneous graphs (ogbn-arxiv, ogbn-products from OGB) and the unsettled debate over whether simple MLPs with neighborhood features can match GNNs, as shown by SIGN and SGC ablations.