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Deep Learning has been a subject of interest in solving a lot of complex machine learning problems, more recently on graph data. However most of the solutions are either supervised or semi-supervised which rely highly on labels in the data, causing over-fitting and overall weak robustness. Self-Supervised Learning (SSL) is an up-and-coming solution which mines useful information from unlabelled data making it a very interesting choice in the field of graph data.

What makes self-supervised learning more suitable for graph data?

Getting labelled graph…

Gayathri Pulagam

Data Science at SJSU

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