GNNs: Classifying Graph Nodes with the Spektral Library

Python    |    Intermediate
  • 6 videos | 42m 31s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 2 users Rating 4.0 of 2 users (2)
Machine learning (ML) models can be used to extract insights from your graph data. Use this course to learn how to build, train, and evaluate a multi-label classification model using a graph convolutional network (GCN) constructed using the Spektral Python library. Begin by structuring a Spektral dataset for machine learning and learn how data is modeled using an adjacency matrix and feature vectors. Explore how to assign instances of your data to training, validation, and test sets using masks applied to your dataset instance. Construct a graph neural network (GNN) with input layers for the adjacency matrix and features and a GCN convolutional layer and use it to perform node classification. Discover how node features, the edges of the graph, and the structure of the neural network affect the performance of the classification model. Upon completion, you'll be able to prepare a graph structure for use in an ML model and define the factors which can improve the accuracy of model predictions.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recognize the structure required to feed graph data into a graph convolutional network (gcn) model
    Set up the different layers of a graph convolutional network (gcn) in order to perform node classification
  • Identify various factors which can influence the quality of predictions made by a gcn model
    Build a gcn model containing multiple dropout layers
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 44s
  • 10m 57s
    After completing this video, you will be able to recognize the structure required to feed graph data into a graph convolutional network (GCN) model. FREE ACCESS
  • Locked
    3.  Building a GCN Classification Model
    10m 4s
    In this video, find out how to set up the different layers of a graph convolutional network (GCN) in order to perform node classification. FREE ACCESS
  • Locked
    4.  Training and Evaluating a GCN Model
    11m 27s
    In this video, you will learn how to identify various factors which can influence the quality of predictions made by a GCN model. FREE ACCESS
  • Locked
    5.  Adding Complexity to a GCN Model
    5m 15s
    Find out how to build a GCN model with multiple dropout layers. FREE ACCESS
  • Locked
    6.  Course Summary
    2m 5s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course GNNs: An Introduction to Graph Neural Networks
Rating 4.3 of 6 users Rating 4.3 of 6 users (6)
Book Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with Python and R
Channel Introduction to Neural Networks Bootcamp
Rating 1.0 of 1 users Rating 1.0 of 1 users (1)