Network Embedding Using Sparse Approximations of Random Walks

• URL: http://arxiv.org/abs/2308.13663v1
• Date: Fri, 25 Aug 2023 20:35:45 GMT
• Title: Network Embedding Using Sparse Approximations of Random Walks
• Authors: Paula Mercurio and Di Liu
• Abstract summary: We propose an efficient numerical implementation of Network Embedding based on commute times, using sparse approximation of a diffusion process on the network obtained by a modified version of the diffusion wavelet algorithm. We demonstrate the efficacy of this method for data clustering and multi-label classification through several examples, and compare its performance over existing methods in terms of efficiency and accuracy.
• Score: 2.676713226382288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose an efficient numerical implementation of Network Embedding based on commute times, using sparse approximation of a diffusion process on the network obtained by a modified version of the diffusion wavelet algorithm. The node embeddings are computed by optimizing the cross entropy loss via the stochastic gradient descent method with sampling of low-dimensional representations of green functions. We demonstrate the efficacy of this method for data clustering and multi-label classification through several examples, and compare its performance over existing methods in terms of efficiency and accuracy. Theoretical issues justifying the scheme are also discussed.

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