Despite the rich line of research on out-of-distribution (OOD) detection on image, text, and time series, the literature on node-level detection for graph data is still relatively limited. To fill this gap, we introduce TopoOOD which focuses on graph topology and neighborhood context while integrating node-wise features to discern OOD instances. Meanwhile, we enrich the experiment settings by splitting in-distribution (ID) and OOD data based on distinct topological distributions, thereby establishing new benchmarks for a comprehensive analysis of OOD discriminators. It is designed to thoroughly assess the performance of these discriminators under varying real-world distribution shifts, providing a rigorous evaluation of methodologies in the emerging node-level OOD field. Our experimental results show an outstanding performance among datasets, as evidenced by up to a 15% increase in the AUROC and a 50% decrease in the FPR compared to existing state-of-the-art approaches.