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Introduction

  • Graph Neural Networks are the current hot topic. [1] And this interest is surely justified as GNNs are all about latent representation of the graph in vector space.
  • Representing an entity as a vector is nothing new. There are many examples like word2vec and Gloves embeddings in NLP which transforms a word into a vector.
  • What makes such representation powerful are,
    • these vectors incorporate a notion of similarity among them i.e. two words who are similar to each other tend to be closer in the vector space (dot product is large), and
    • they have application in diverse downstream problems like classification, clustering, etc.
  • This is what makes GNN interesting, as while there are many solutions to embed a word or image as a vector, GNN laid the foundation to do so for graphs.

References

[1] EasyAI — GNN may be the future of AI