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The net4Lap Architecture. Given an input kNN graph, the process begins with a neural embedding process (stochastic gradient descent with negative sampling) yields a harmonic version that feeds the Laplacian regularizer step. The result is a denser graph suitable either for ranking or for obtaining an improved kNN graph which in turns feeds stochastic gradient descend for re-ranking.
