Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning.

ABSTRACT:

Motivation

Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers d

SUBMITTER: Cappelletti L 

PROVIDER: S-EPMC10994718 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature