Graph Machine Learning (GML) along with Algorithms and their Applications

Graph Network

“Graph Machine Learning is the future of AI. It can be used to extract insights from large-scale, complex data and make predictions that traditional methods cannot match.”

— Demis Hassabis, CEO of DeepMind (DeepMind Safety Research)

Graph Machine Learning (GML) is a rapidly growing field that combines the power of machine learning with the representation of data in the form of graphs. Graphs are a powerful tool for modeling complex systems, as they can capture the relationships and interactions between different entities.

Why Graph Machine Learning is better than the Classical Approach 🤔❓

Graph Machine Learning (GML) is often considered to be better than Classical Machine Learning for several reasons:

1. Handling complex relationships: Graphs are a natural way to represent complex relationships between entities. GML algorithms are designed to take advantage of this property and can handle relationships that are difficult or impossible to represent using traditional methods.
2. Handling missing data: In graph data, it is common to have missing data or incomplete information. GML algorithms are robust to this type of data and can still extract meaningful…