ScholarGraph-Synthetic

ScholarGraph-Synthetic is a fully synthetic citation-network dataset for graph-based semi-supervised node classification. Nodes represent papers, each assigned to one of eight research-topic classes. Edges represent citations, generated with a homophilous Stochastic Block Model: papers preferentially cite others in the same topic, producing the community structure characteristic of real citation graphs, with an average node degree comparable to the real Cora/Citeseer benchmarks. Node features are a 300-dimensional class-conditional bag-of-words vector, deliberately blended with a shared background vocabulary so that features alone are a weak classifier. This dataset ships a matched-pair ablation (identical features, split, and layer sizes, only the propagation mechanism differs) confirming that a graph-blind MLP baseline scores far lower than GCN/GAT, i.e. the graph structure is doing real work rather than being a redundant add-on. The dataset follows the standard low-label-rate transductive split convention: roughly 20 labeled training nodes per class, separate validation and test sets, and remaining nodes unlabeled but part of the graph. Contains no real bibliographic, personal, or proprietary data.