So in brief here is the outline of the blog:

  • What is GraphSage
  • Importance of Neighbourhood Sampling
  • Getting Hands-on Experience with GraphSage and PyTorch Geometric Library
  • Open-Graph-Benchmark’s Amazon Product Recommendation Dataset
  • Creating and Saving a model
  • Generating Graph Embeddings Visualizations and Observations

The key idea behind the graph representation learning is to learn a mapping function that embeds nodes, or entire (sub)graphs (from non-euclidean), as points in low-dimensional vector space (to embedding space). The aim is to optimize this mapping so that nodes which are nearby in the original network should also remain close to each other in the embedding space (vector space), while shoving unconnected nodes apart. Therefore by doing this, we can preserve the geometric…

Continue reading: https://towardsdatascience.com/a-comprehensive-case-study-of-graphsage-algorithm-with-hands-on-experience-using-pytorchgeometric-6fc631ab1067?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com