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This article mainly introduces "what is graph neural network in artificial intelligence". In daily operation, I believe that many people have doubts about what is graph neural network in artificial intelligence. The editor consulted all kinds of data and sorted out simple and useful operation methods. I hope it will be helpful for you to answer the doubts about what is graph neural network in artificial intelligence. Next, please follow the editor to study!

1. The definition of graph neural network.

GNN full name graph neural network, where G is the meaning of Graph, GNN is important because the graph is very important. Graph is a very important data structure in computer science. Computer science has a compulsory basic course called discrete Mathematics, which sounds like a branch of mathematics, but where is the boundary of discrete Mathematics? there is no unified conclusion yet. But there is one knowledge point that all versions of discrete Mathematics textbooks will not miss, and that is Graph Theory, which discusses a kind of data structure called graph. The "graph" in GNN refers to the "graph" of graph theory.

So what on earth is "picture"? There are two things, vertex (Vertex) and edge (Edge). The so-called vertices are the nodes in the network topology diagram, such as PCs, servers and routers in the network topology diagram, and the so-called edges are the lines that connect these network nodes. Therefore, the application of graph is very wide, and the network topology graph is a very typical graph structure.

2. The rise of graph neural networks.

The emergence of graph neural network is essentially the rise of a new technology, so why launch this new technology? Launch a new technology, the subtext is that the original technology is inadequate, let's take a look at the shortcomings of CNN and RNN. To put it bluntly, it is the data structure, and the model is to feed the data, which we all know. However, the existing deep learning models, whether CNN, RNN, or whatever, have a requirement for the data structure, which must be Euclidean structure. What is square is the Euclid structure. The parade phalanx of military training is one person next to one person horizontally and longitudinally. This is the typical Euclid structure. The graph is non-Euclidean structure, so there is no way to use the traditional depth model to deal with. Therefore, the researchers developed a graph neural network.

3. The use of graph neural network

In recent years, deep learning has brought the successful application of face recognition, speech assistant and machine translation. Behind these three kinds of scenes, there are three kinds of data: image, voice and text. The key to the breakthrough of deep learning in these three scenarios is the end-to-end learning mechanism behind it. In addition, the industry believes that large-scale graph neural network is a powerful reasoning method for cognitive intelligent computing. The graph neural network extends the deep neural network from dealing with traditional unstructured data to higher-level structured data. Not only that, the graph also has a strong ability of semantic visualization, which is shared by all GNN models. For example, in the case of abnormal trading account identification, GNN can visualize the local sub-graph of an account after judging it as an abnormal account.

At this point, the study of "what is a graph neural network in artificial intelligence" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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