About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Isaac Ronald Ward
- sl:arxiv_num : 2010.05234
- sl:arxiv_published : 2020-10-11T12:36:17Z
- sl:arxiv_summary : Graph neural networks (GNNs) have recently grown in popularity in the field
of artificial intelligence 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 the average deep learning enthusiast by collating and
presenting details on the motivations, concepts, mathematics, and applications
of the most common types of GNNs. Importantly, we present this tutorial
concisely, alongside worked code examples, and at an introductory pace, thus
providing a practical and accessible guide to understanding and using GNNs.@en
- sl:arxiv_title : A Practical Guide to Graph Neural Networks@en
- sl:arxiv_updated : 2020-10-11T12:36:17Z
- sl:bookmarkOf : https://arxiv.org/abs/2010.05234
- sl:creationDate : 2020-10-15
- sl:creationTime : 2020-10-15T00:07:48Z
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