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Graph Neural Network: One Step Further In Deep Learning

In the development of artificial intelligence, the learning process is essential. The machine learning and especially deep learning are used…

By TrendsTechBlog , in MARKETING , at May 18, 2020 Tags: ,

In the development of artificial intelligence, the learning process is essential. The machine learning and especially deep learning are used to teach the software to think independently, training their algorithms. For example, the first technology is applied to facial recognition. Many machine learning approaches are based on artificial neural networks, in which the software algorithms are designed as a network of nodes that mimics the human nervous system. A new approach is called graph neural networks, or graphical neural networks.

How Do Graph Neural Networks Work

Graph neural networks or GNN (Graphic Neural Networks) are a new form of artificial neural networks (artificial neural networks) based on graphs. To understand GNN, you must first know what is meant by a graph in the field of computing, in which the term refers to a specific way of structuring data: a graph consists of several data points (nodes, or vertices in English). ) that are connected (by edges, or edges in English) and therefore form pairs. You will see it clearer with this example: person A and person B can be represented as points in a graph. If they are connected, it means that there is some relationship between them. If the connections are removed, only one set of people or data remains.

A tree is a well-known form of a graph. In this structure, the nodes are connected so that there is only one path (that can traverse multiple nodes) between point A and point B. The edges can follow either direction or none. In a graph, relationships are as important as the data itself. Both edges and nodes can be assigned attributes.

Therefore, the graph is ideal to represent real conditions, which is precisely one of the challenges of deep learning: getting the software to understand natural conditions. This is just what a neural network graph does, where nodes collect information from neighboring nodes because they exchange messages with each other regularly. In this way, the neural network graph can learn: the data is transmitted and incorporated into the properties of the corresponding node.

Where Are Graph Neural Networks Used

Until now, scientists have mainly focused on investigating the possibilities of graph neural networks, proposing the most diverse applications. In general, using GNN makes sense when situations or processes in which relationships play an important role must be represented through neural networks.

  • Financial Market: By understanding the transactions, it is possible to obtain a more reliable market forecast.
  • Search Engines: to assess the importance of web pages, it is essential to take into account the links between the pages.
  • Social Media: understanding the relationships between people can help optimize social media.
  • Chemistry: The composition of the molecules can be shown in graphs and therefore transferred to the GNN.
  • Knowledge: understanding how information is related is essential to correctly represent knowledge.

Graph neural networks are also used in image and speech recognition. In some circumstances, unstructured natural information can be better processed by GNNs than by traditional neural networks.

Advantages & Disadvantages Of Graph Neural Networks

Graph neural networks help deal with challenges that traditional neural networks have so far been unable to overcome, or at least in part. In the past, graph-based data could not be processed correctly, because the relationships between the data were not sufficiently weighted. With GNN, the so-called edges are as important as the nodes themselves.

However, graph neural networks cannot solve all the problems that neural networks entail. In particular, the black box problem is likely to continue to emerge: It is difficult to understand how a (graphical) neural network makes the final decision since internal processes based on complex algorithms can hardly be understood from the outside.

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