Network Embedding Using Sparse Approximations of Random Walks
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 (SGD) 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.
Fields of Study & Subjects
Complex Network Analysis Techniques
Neural Networks Stability and Synchronization
Functional Brain Connectivity Studies
Publication details
- Journal / source
- Communications in Computational Physics
- Publisher
- Global Science Press
- Volume
- 40
- Issue
- 1
- Pages
- 176–198
- Publication date
- 10 April 2026
- Language
- EN