  Graph Neural Networks (GNNs) have excelled in predicting graph properties in
various applications ranging from identifying trends in social networks to drug
discovery and malware detection. With the abundance of new architectures and
increased complexity, GNNs are becoming highly specialized when tested on a few
well-known datasets. However, how the performance of GNNs depends on the
topological and features properties of graphs is still an open question. In
this work, we introduce a comprehensive benchmarking framework for graph
machine learning, focusing on the performance of GNNs across varied network
structures. Utilizing the geometric soft configuration model in hyperbolic
space, we generate synthetic networks with realistic topological properties and
node feature vectors. This approach enables us to assess the impact of network
properties, such as topology-feature correlation, degree distributions, local
density of triangles (or clustering), and homophily, on the effectiveness of
different GNN architectures. Our results highlight the dependency of model
performance on the interplay between network structure and node features,
providing insights for model selection in various scenarios. This study
contributes to the field by offering a versatile tool for evaluating GNNs,
thereby assisting in developing and selecting suitable models based on specific
data characteristics.
