Abstract

The biomedical network is becoming a fundamental tool to represent sophisticated biosystems, while Random Walk (RW) models on it are becoming a sharp sword to address such challenging issues as gene function annotation, drug target identification, and disease biomarker recognition. Recently, numerous random walk models have been proposed and applied to biomedical networks. Due to good performances, the random walk is attracting increasing attentions from multiple communities. In this survey, we firstly introduced various random walk models, with emphasis on the PageRank and the random walk with restart. We then summarized applications of the random work RW on the biomedical networks from the graph learning point of view, which mainly included node classification, link prediction, cluster/community detection, and learning representation of the node. We discussed briefly its limitation and existing issues also.

Keywords: Random walk, network embedding, link prediction, clustering, node classification, node representation.

Graphical Abstract