Accurately modeling biased random walks on weighted networks using node2vec.

ABSTRACT:

Motivation

Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction networks, are dense, weighted graphs, node2vec is fundamentally limited in its ability to use edge weights during the biased random walk generation process, thus under-using all the information in the network.

Results

Here, we present node2vec+, a natural extension of node2vec that accounts for edge weights when calculating walk biases and reduces to node2vec in the cases of unweighted graphs or unbiased walks. Using two synthetic datasets, we empir

SUBMITTER: Liu R 

PROVIDER: S-EPMC9891245 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature