[R] A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics

**Paper**: [https://arxiv.org/abs/2311.02127](https://arxiv.org/abs/2311.02127)

**Abstract**:

>In recent years, tasks of machine learning ranging from image processing & audio/video analysis to natural language understanding have been transformed by deep learning. The data content in all these scenarios are expressed via Euclidean space. However, a considerable amount of application data is structured in non-Euclidean space and is expressed as graphs, e.g. dealing with complicated interactions & object interdependencies. Modelling physical systems, learning molecular signatures, identifying protein interactions and predicting diseases involve utilising a model …

abstract analysis application application data audio data deep learning graphs image image processing interactions language language understanding machine machine learning machinelearning natural natural language non-euclidean processing space tasks understanding video video analysis