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Segmentation of 3D model models has applications, e.g., in mesh editing and 3D model retrieval. Unsupervised, automatic segmentation of 3D models can be useful. However, some applications require user-guided, interactive segmentation that captures user intention. This paper presents a supervised, local-geometry aware segmentation algorithm for 3D mesh models. The algorithm segments manifold meshes based on interactive guidance from users. The method casts user-guided mesh segmentation as a semi-supervised learning problem that propagates segmentation labels given to a subset of faces to the unlabeled faces of a 3D model. The proposed algorithm employs Zhou's Manifold Ranking  algorithm, which takes both local and global consistency in high-dimensional feature space for the label propagation. Evaluation using a 3D model segmentation benchmark dataset has shown that the method is effective, although achieving interactivity for a large and complex mesh requires some work.