# A Practical Tutorial on Graph Neural Networks

arXiv: https://arxiv.org/abs/2010.05234 · HTML: https://mlsanity.com/paper/2010.05234/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Predictive Coding Networks and Inference Learning: Tutorial and Survey](https://mlsanity.com/paper/2407.04117/) (2024)
- [A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness](https://mlsanity.com/paper/2406.09675/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [A Graph-Based Approach to Alert Contextualisation in Security Operations Centres](https://mlsanity.com/paper/2509.12923/) (2025)
- [Complex Networks for Pattern-Based Data Classification](https://mlsanity.com/paper/2503.05772/) (2025)
- [RiskNet: Neural Risk Assessment in Networks of Unreliable Resources](https://mlsanity.com/paper/2201.12263/) (2022)
- [Structural Embeddings of Tools for Large Language Models](https://mlsanity.com/paper/2308.00447/) (2023)
- [Embedding Learning on Multiplex Networks for Link Prediction](https://mlsanity.com/paper/2602.01922/) (2026)

## Abstract (arXiv)

Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements represent a departure from traditional deep learning techniques. This tutorial exposes the power and novelty of GNNs to AI practitioners by collating and presenting details regarding the motivations, concepts, mathematics, and applications of the most common and performant variants of GNNs. Importantly, we present this tutorial concisely, alongside practical examples, thus providing a practical and accessible tutorial on the topic of GNNs.