GNNs: Classifying Graph Nodes with the Spektral Library

Python 3.6+    |    Intermediate
  • 6 videos | 42m 31s
  • Includes Assessment
  • Earns a Badge
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.


  • 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


  • 2m 44s
  • 10m 57s
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    3.  Building a GCN Classification Model
    10m 4s
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    4.  Training and Evaluating a GCN Model
    11m 27s
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    5.  Adding Complexity to a GCN Model
    5m 15s
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    6.  Course Summary
    2m 5s


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