Title: Detecting Out-of-Distribution Data with Semi-supervised Graph “Feature" Networks. Abstract: Anomalous and out-of-distribution (OOD) data present a significant challenge to the robustness of decisions taken by deep neural networks, with myriad real-world consequences. State-of-the-art OOD detection techniques use embeddings learned by large pre-trained transformers. We demonstrate that graph structures and topological properties can be leveraged to detect both far-OOD and near-OOD data reliably, simply by characterising each data point (image) as a network of related features (visual concepts). Furthermore, we facilitate human-in-the-loop machine learning by expressing this data to comprise high-level domain-specific concepts. We obtained \textit{97.95\% AUROC} on far-OOD and \textit{98.79\% AUROC} on near-OOD detection tasks based on the LSUN dataset (comparable to the performance of state-of-the-art techniques).