Graph embedding

Graph embedding is like taking a picture of a bunch of dots and lines that are all connected in a certain way. It's like a map of a place, but instead of roads and buildings, it shows how different things are related to each other.

Think about a puzzle where you have to connect dots with lines to make a picture. When you're done, you have a complete image made up of several dots and lines.

Now imagine that instead of lines and dots, you have data points such as people, products, or web pages. Graph embedding is like figuring out how all these things are related to each other and what they have in common. This helps sort them in a way that is easy to analyze and understand.

The graph embedding process involves taking these data points and converting them into a numerical format. This numerical representation captures the relationships and similarities between each point in the graph.

For example, if we have a graph of people and their interests, we can represent each person and their interests as numbers. Then we can plot these numbers on a graph, where the distance between each point represents the similarity between each pair of people.

This helps us understand how different people are related to each other based on the things they like. We can also use graph embedding to predict what people might be interested in based on the patterns we see in their behavior.

Overall, graph embedding is a way of simplifying complex data and transforming it into something that is easy to understand and analyze.