A dynamic quantile filtering method for commodity co-occurrence network

Abstract

This invention discloses a dynamic quantile filtering method for product co-occurrence networks, comprising: firstly, constructing a product co-occurrence network based on the co-occurrence relationships of products in orders; then, employing a dynamic quantile filtering strategy to denoise the network, specifically: obtaining the co-occurrence frequency weights of all product pairs and forming a co-occurrence matrix; extracting all non-zero co-occurrence frequency weights for each node; defining a local adaptive threshold based on the quantiles of these weights; retaining edges greater than or equal to the threshold; resetting the weights of edges less than the threshold to zero; and retaining isolated nodes; symmetricizing the adjacency matrix using an intersection strategy to obtain a weighted adjacency matrix; updating the network to obtain the denoised product co-occurrence network; and finally, using the Louvain algorithm to perform community partitioning on the denoised network to obtain a product community network. This invention can adaptively filter out weak connections and noisy edges in the product co-occurrence network, effectively improving the accuracy and robustness of community detection.

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