Dear Geetanjali P.Kale .
The recent success of neural networks has boosted research on pattern recognition and data mining.
Machine learning tasks, like object detection, machine translation, and speech recognition, have been given new life with end-to-end deep learning paradigms like CNN, RNN, or autoencoders.
Deep Learning is good at capturing hidden patterns of Euclidean data (images, text, videos).
But what about applications where data is generated from non-Euclidean domains, represented as graphs with complex relationships and interdependencies between objects?
That’s where Graph Neural Networks (GNN) come in, which we’ll explore in this article. We’ll start with graph theories and basic definitions, move on to GNN forms and principles, and finish with some applications of GNN..
https://neptune.ai/blog/graph-neural-network-and-some-of-gnn-applications
Regards,
Shafagat
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