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The k-nearest neighbor graph (KNNG) on high-dimensional data is a data structure widely used in many applications such as similarity search, dimension reduction and clustering. Due to its increasing popularity, several methods under the same framework have been proposed in the past decade. This framework contains two steps, i.e. building an initial KNNG (denoted as ) and then refining it by neighborhood propagation (denoted as ). However, there remain several questions to be answered. First, itarXiv:2112.02234v1 fatcat:od4skj4worgvfc7dbonwof7y2y