Machine Learning on Graphs

GNN for Document Classification you own this product

This project is part of the liveProject series Machine Learning on Graphs for NLP
intermediate Python • basic PyTorch Geometric • basic NLP and Graph Theory • intermediate deep learning with PyTorch • basic Colab
skills learned
node classification by text embedding signals and citation graph structure signals • build a GNN classifier with PyTorch Geometric
Sujit Pal
1 week · 6-8 hours per week · ADVANCED
filed under

placing your order...

Don't refresh or navigate away from the page.
liveProject This project is part of the liveProject series Machine Learning on Graphs for NLP liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
GNN for Document Classification (liveProject) added to cart
continue shopping
adding to cart

choose your plan


only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • GNN for Document Classification eBook for free
Look inside

In this liveProject, you’ll implement a Graph Neural Network (GNN). This powerful model will allow you to use the document content from the first liveProject combined with the structure of the citation graph from the second liveProject to build an even more powerful model—one that will predict the sub-field of statistics of each of your customers’ papers.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Sujit Pal
Sujit Pal is a data scientist at Elsevier Labs, an advanced technology group within Elsevier. His areas of interest are Information Retrieval (IR), Natural Language Processing (NLP), and Machine Learning (ML). At Elsevier, he has worked on projects on Image Search and Retrieval, Question Answering, Automated Knowledge Graph Construction, and more. He first became aware of the effectiveness of Graph techniques in NLP about two years ago and has had quite a lot of success with it since. He’s active in various Data Science, ML, and IR communities, and has presented at conferences including PyData, ODSC, Haystack, Graphorum, and Spark Summit. Prior to this liveProject series, he co-authored two books on Deep Learning.


This liveProject is for Natural Language Processing (NLP) practitioners who have an intermediate level of knowledge of the Python programming language (especially in the NLP domain) and who are ready to uplevel their NLP skills by applying GNNs for document classification. To begin this liveProject, you’ll need to be familiar with the following:

  • Intermediate Python
  • Intermediate PyTorch
  • Basic PyTorch Geometric
  • basic NLP and Graph Theory
  • intermediate Deep Learning

you will learn

In this liveProject, you’ll learn skills and techniques for using GNNs for classifying the nodes in a graph:

  • Building the dataset from the data
  • Building the GNN classifier
  • Training and evaluating the classifier


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.