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Graph machine learning is a type of machine learning that uses graph data structures to represent and learn from data. Graphs are a powerful data structure for representing data and can be used to model many real-world phenomena.

What is a graph data?

A graph is a data structure that can be used by a number of algorithms, including neural networks, for various tasks such as classification, clustering, and regression. Graphs are a powerful data structure for representing data and can be used to model many real-world phenomena.

Here is an example of how graph data can be ingested by algorithms:

Data (graph, words) -> Real number vector -> Deep neural network

A graph can be embedded with algorithms into a vector of real values (similar to the embedding of a word). In the end, a vector representation of each node in the graph will be obtained with some information preserved. As soon as the real number vector has been obtained, it can be fed into the neural network for processing.

Graph machine learning

Graph machine learning algorithms are able to learn from data that is represented as a graph. This type of machine learning is well suited for problems that can be represented as a graph, such as social network analysis, link prediction, and recommendation systems.

Graph machine learning algorithms usually take as input a graph with node and edge features. The algorithms then learn to make predictions about the graph, such as which nodes are likely to be connected in the future.

There are many different types of graph machine learning algorithms, including graph convolutional networks, graph neural networks, and deep graph kernels. These algorithms have been used to achieve state-of-the-art results on a variety of tasks, such as link prediction and node classification.

Graph machine learning is a promising area of machine learning that is still in its early stages of development. There is a lot of potential for this type of machine learning to be applied to a wide range of real-world problems.

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