Introduction

Learning from graph-structured data is a challenging and ubiquitous task. Its application domains vary from social network link prediction to molecular function group analysis, from polypharmacy side effect prediction to biological networks. This blog discusses several representation learning methods on graph-structured data. The atlas of graph representation learning methods are generally enveloped in multiple categories [1].

Reference

[1] William L. Hamilton, Rex Ying, Jure Leskovec. Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin. 2017.