Network Embedding Using Sparse Approximations of Random Walks
This work addresses network analysis challenges for researchers and practitioners by providing a more efficient embedding method, though it appears incremental as it builds on existing diffusion-based approaches.
The authors tackled the problem of network embedding by proposing an efficient numerical implementation based on commute times using sparse approximations of random walks, achieving improved efficiency and accuracy in data clustering and multi-label classification tasks compared to existing methods.
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.