Covers: theory of Mixed-method Link Prediction
Estimated time needed to finish: 20 minutes
Questions this item addresses:
  • How can graph classification be used to predict links?
How to use this item?

This notebook covers:

  • Introduction to SEAL framework for link prediction
  • Creating Induced Subgraphs
  • Classification of Graphs using GNNs
Author(s) / creator(s) / reference(s)
Karim Khayrat
Programming Languages: PyTorch
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Link Prediction using Graph Neural Networks

Contributors
Total time needed: ~1 hour
Objectives
This recipe will walk you through the typical techniques used for predicting relationships between entities
Potential Use Cases
recommendation systems, friend suggestion on social media
Who is This For ?
INTERMEDIATEData Scientists, Machine Learning Engineers
Click on each of the following annotated items to see details.
Resource Asset3/3
REPO 1. Link Prediction as Graph Prediction
  • How can graph classification be used to predict links?
20 minutes
REPO 2. Embedding Based Link Prediction
  • How graph auto-encoder can be used to predict links?
20 minutes
PAPER 3. Review on Learning and Extracting Graph Features for Link Prediction
  • What are the ways link prediction work?
20 minutes

Concepts Covered

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