Examining the Effects of Degree Distribution and Homophily in Graph
  Learning Models
Mustafa Yasir John Palowitch Anton Tsitsulin Long Tran-Thanh Bryan Perozzi
Despite a surge in interest in GNN development, homogeneity in benchmarking
datasets still presents a fundamental issue to GNN research. GraphWorld is a
recent solution which uses the Stochastic Block Model (SBM) to generate diverse
populations of synthetic graphs for benchmarking any GNN task. Despite its
success, the SBM imposed fundamental limitations on the kinds of graph
structure GraphWorld could create.
  In this work we examine how two additional synthetic graph generators can
improve GraphWorld's evaluation; LFR, a well-established model in the graph
clustering literature and CABAM, a recent adaptation of the Barabasi-Albert
model tailored for GNN benchmarking. By integrating these generators, we
significantly expand the coverage of graph space within the GraphWorld
framework while preserving key graph properties observed in real-world
networks. To demonstrate their effectiveness, we generate 300,000 graphs to
benchmark 11 GNN models on a node classification task. We find GNN performance
variations in response to homophily, degree distribution and feature signal.
Based on these findings, we classify models by their sensitivity to the new
generators under these properties. Additionally, we release the extensions made
to GraphWorld on the GitHub repository, offering further evaluation of GNN
performance on new graphs.