  Manifold Learning occupies a vital role in the field of nonlinear
dimensionality reduction and its ideas also serve for other relevant methods.
Graph-based methods such as Graph Convolutional Networks (GCN) show ideas in
common with manifold learning, although they belong to different fields.
Inspired by GCN, we introduce neighbor propagation into LLE and propose Local
Neighbor Propagation Embedding (LNPE). With linear computational complexity
increase compared with LLE, LNPE enhances the local connections and
interactions between neighborhoods by extending $1$-hop neighbors into $n$-hop
neighbors. The experimental results show that LNPE could obtain more faithful
and robust embeddings with better topological and geometrical properties.
