Graph Neural Networks: The Next Step In Deep Learning
The learning process is crucial to developing artificial intelligence. Machine learning, especially deep learning, is used to train algorithms and thus gives the software its own thinking. Facial recognition, for example, is based on this kind of technology.
Many machine learning solutions work on artificial neural networks. The software algorithm is designed as a network of nodes (nodes), like the structure of the human nervous system. There is a new type of network called graph neural network (GNN) or graph neural network.
How does a neural network graph work?
Graph Neural Network (GNN) is a new type of artificial neural network based on graphs. Simple example: person A and person B can be specified as points on the graph. Their relationship is the link between these points. When the relationship disappears,
we end up getting data with a set of people. A well-known subcategory of graphs is trees. This time the nodes are connected to each other in such a way that there is only one path between point A and point B (even for multiple nodes). Corners can only follow one direction or have no direction. In graphs, relationships are just as important as the data itself. Like each node, each edge can have properties.
Therefore, graphs are particularly suitable for representing concrete realities. It is this property that advances deep learning. This is what neural network graphs allow. In GNN, nodes regularly exchange information with each other, so they collect information from their neighbors. This is how a graphed neural network can learn. The information is relayed and recorded in the properties of each node.
In what cases are neural networks used?
Until now, scientists have been the first to be interested in the possibility of graphing neural networks. However, there are many possibilities for the application.
It turns out that GNNs are relevant as soon as relationships play an important role in a situation or process and the whole must be represented in a neural network.
Financial Markets: Market forecasting can gain credibility by understanding the transaction.
Search Engines: To assess the importance of a website, the relationship between pages is crucial.
Social Networks: A better understanding of the relationships between people can help you optimize your social networks.
Chemistry: The composition of the molecule can be graphed, so it is transposed by GNN.
Knowledge: Ideally, it is important to understand the links between the information in order to be able to use it.
Neural networks are already used for image and speech recognition. Under certain conditions, unstructured natural information is better processed using GNN than using conventional neural networks.
Advantages and Disadvantages
Graphing neural networks enables advancements that can be considered rudimentary in conventional neural networks. Previously, we couldn’t properly process the data in the graph because we couldn’t fully consider the relationships between the data.
With GNNS the edges are now as accurate as the nodes themselves. Another problem inherent in neural networks has to do with GNN. Specifically, the black box problem has not been solved. Not to mention graphically, it’s still not clear how the neural network picks up the final estimate. It is almost impossible to keep track of the processes taking place in the system.
The learning process is crucial to developing artificial intelligence. Machine learning, especially deep learning, is used to train algorithms and thus gives the software its own thinking. Facial recognition, for example, is based on this kind of technology.
Many machine learning solutions work on artificial neural networks. The software algorithm is designed as a network of nodes (nodes), like the structure of the human nervous system. There is a new type of network called graph neural network (GNN) or graph neural network.
How does a neural network graph work?
Graph Neural Network (GNN) is a new type of artificial neural network based on graphs. Simple example: person A and person B can be specified as points on the graph. Their relationship is the link between these points. When the relationship disappears,
we end up getting data with a set of people. A well-known subcategory of graphs is trees. This time the nodes are connected to each other in such a way that there is only one path between point A and point B (even for multiple nodes).
Corners can only follow one direction or have no direction. In graphs, relationships are just as important as the data itself. Like each node, each edge can have properties.
Therefore, graphs are particularly suitable for representing concrete realities. It is this property that advances deep learning. This is what neural network graphs allow. In GNN, nodes regularly exchange information with each other, so they collect information from their neighbors. This is how a graphed neural network can learn. The information is relayed and recorded in the properties of each node.
In what cases are neural networks used?
Until now, scientists have been the first to be interested in the possibility of graphing neural networks. However, there are many possibilities for the application. It turns out that GNNs are relevant as soon as relationships play an important role in a situation or process and the whole must be represented in a neural network.
Financial Markets: Market forecasting can gain credibility by understanding the transaction.
Search Engines: To assess the importance of a website, the relationship between pages is crucial.
Social Networks: A better understanding of the relationships between people can help you optimize your social networks.
Chemistry: The composition of the molecule can be graphed, so it is transposed by GNN.
Knowledge: Ideally, it is important to understand the links between the information in order to be able to use it.
Neural networks are already used for image and speech recognition. Under certain conditions, unstructured natural information is better processed using GNN than using conventional neural networks.
Advantages and Disadvantages
Graphing neural networks enables advancements that can be considered rudimentary in conventional neural networks. Previously, we couldn’t properly process the data in the graph because we couldn’t fully consider the relationships between the data.
With GNNS the edges are now as accurate as the nodes themselves. Another problem inherent in neural networks has to do with GNN. Specifically, the black box problem has not been solved. Not to mention graphically, it’s still not clear how the neural network picks up the final estimate. It is almost impossible to keep track of the processes taking place in the system.