We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features. Our proposed method achieves this by compressing the input graph prior to embedding it, e ectively avoiding troublesome embedding con gurations (i.e. local minima) which can pose problems to non-convex optimization. HARP works by nding a smaller graph which approximates the global structure of its input. This simpli ed graph is used to learn a set of initial representations, which serve as good initializations for learning representations in the original, detailed graph. We inductively extend this idea, by decomposing a graph in a series of levels, and then embed the hierarchy of graphs from the coarsest one to the original graph. HARP is a general meta-strategy to improve all of the state-ofthe-art neural algorithms for embedding graphs, including Deep-Walk, LINE, and Node2vec. Indeed, we demonstrate that applying HARP's hierarchical paradigm yields improved implementations for all three of these methods, as evaluated on classi cation tasks on real-world graphs such as DBLP, BlogCatalog, and CiteSeer, where we achieve a performance gain over the original implementations by up to 14% Macro F1.
Work of REF proposes to learn low dimensional embeddings of a graph's nodes.
HARP: Hierarchical Representation Learning for Networks
ArXiv
ArXiv
Computer Science