Semi-supervised node classification on graphs is a complex interplay between graph structure, node features and class-assortative (homophily) properties, and the flexibility of a model to capture these nuances. Modern datasets used to push the frontier for these tasks exhibit diverse properties across these fronts, making it challenging to study how these properties individually and jointly influence performance of modern embedding-based methods like graph neural networks (GNNs) for this task. In this work-in-progress, we propose an intuitive and flexible scale-free graph generation model, CaBaM, which enables simulation of class-assortative and attributed graphs via the well-known Barabasi-Albert model. We show empirically and theoretically how our model can easily describe a variety of graph types, while imbuing the generated graphs with the necessary ingredients for attribute, topology, and label-aware semi-supervised node-classification.