Immersive Analytics (IA) utilises stereoscopic 3D (S3D) graph visualisations in Virtual and Augmented Reality to harness spatial understanding and engagement beyond 2D screen displays. Despite growing research interest, little is known about how users perceive such structures, or how their gaze behaviour relates to task performance. We address this through a series of eye tracking studies on visual analytical tasks over S3D graphs in VR. Our first study, on single-layer graphs, reveals systematic links between gaze behaviour and task performance, with distinct exploration strategies across tasks. We then extend to multilayer graphs and show how their spatial arrangement and complexity reshape gaze behaviour. Finally, by mining the gaze patterns of successful analysts, we enhance the visualisations with targeted visual cues; in a follow-up study, these improve task correctness in the majority of trials. Overall, our findings deepen the understanding of graph perception in immersive environments, from single to complex multilayer structures, and demonstrate how eye tracking can assess human behaviour and inform the design of more effective S3D graph visualisations. The files of this dataset are documented in README.md.