Graphs are such data structures that have vertices (corners), as well as vectors directed from them – edges (vectors). Graphs are described by simple linear functions, easily recognized by most applications. The simplest Graph Neural Networks are data processing systems that obey the functions described above. Without graphs, it is impossible to systematize any database consisting of millions of clusters, such as Internet banking or social networks, where people log in by entering personal data in the query string.
Neural networks are the backbone of AI, meaning they are capable of processing more complex data that goes far beyond simple math dependencies. This includes the classification of graphics, as well as the layers and symbols that make up any content that is visible to the user.
Graphical neural networks allow you to organize and classify data of any complexity, scattered in an arbitrary order, rotated, mirrored, or defragmented in 3D space.
What are graphical neural networks for?
To build a simple graph of a quadratic or linear function, it is enough to substitute a random number for one of the unknowns and use the equation to calculate the second value. Thus, the graphical display of any dependency is a collection of iterations that depend on the forward value and determine the position of the point in space.
The principle of operation of graphical neural networks is approximately the same. The difference concludes in the presence of an infinite set of destinations for finding coordinates – each vector and point that has input data is analyzed for any request.
How do graph neural networks work?
Despite the huge number of iterations, all non-linear functions for graph neural networks obey a certain algorithm. The principle of operation of such technology is based on the following actions:
- Each adjacent node of incoming data processes all the information for the next and previous iterations.
- The system uses algebraic dependencies to calculate the average values of a function during the analysis of adjacent iterations by the interpolation method.
- From the identified node, a directed vector is constructed, which serves as the next cluster for classifying information inputs.
- The vectors are added together to form the resulting vector, which, in turn, is multiplied by the neural network matrix to recognize each random dataset.
- The greater the density of the network of neurons, the higher the accuracy of data processing.
- A non-linear dependence is applied, by a predetermined structure, which provides an accurate classification of the data.
- After the transformation and recognition of each node, the process of integrating the processed data takes place.
- Each node during processing passes through two iterations in the form of neural matrixes – for the current node and neighboring ones.
- When processing the data of the current node, the process of Self-Supervised Learning takes place, when the cluster learns and reads information about itself.
- the specified operation is repeated exactly as many times as necessary for the user to obtain the desired result.
As soon as each node has completed the self-learning procedure, all neighboring clusters, having passed through a dense neural network, are compared with each other, which determines the signs of similarity and allows classifying both symbolic and graphic materials.
The user, in turn, can artificially set the boundary parameters for recognizing certain signs of the user, which ensures increased attention only to the necessary data and ignoring the remaining nodes.
Thus, filtering is performed, which is important for performing an interactive search. The accuracy of processing and the quality of classification depending on the distances between neighboring nodes. That is, if the system builds vectors between closely spaced support points, the coordinates will provide a graph that is as close to reality as possible.
What is the GCN architecture?
A convolutional operator based on data transformation when node coordinates are passed through a pixel grid is called the GCN architecture. This technology is very similar to neural networks, but there are minor differences between these concepts:
- The GCN architecture handles averages for each intermediate node. This entails a loss inaccuracy.
- In GCN, unlike neural networks, it doesn’t analyze the structure of its environment using a multi-step interpolation method but takes into account only node points and vector units with linear dependence.
- The current neighborhood of a node differs from its coordinates and the graph of all other vertices.
The uniqueness of the neural grid lies in the possibility of increasing the number of iterations without binding to the vertices. This dependence is described by a primitive algebraic matrix – the accuracy of data processing increases with an increase in the number of columns and rows.
What are the limitations of the Graph Neural Network?
Even though GNN development technology has come a long way since its inception almost 10 years ago, the industry is still in a partial state. This means that the following restrictions are imposed on the data analysis and classification system:
- Not every data structure or graphic image is stable. In addition to the character set, nodes, and ribs, noise or vibration may occur.
- The matrix does not always recognize and filter out unnecessary spaces, which leads to errors in reading and structuring the result.
- If neighboring nodes differ by negligible values, the matrix may not recognize these changes.
- Despite the high density of the neural network, neighboring iterations can be very small, but at the same time, they are described by segment graphs. This problem causes a lot of difficulties when structuring information, leading to errors that can accumulate and be reflected in the efficiency of the entire system.
Despite the recent appearance, the popularity of neural network graphs is growing every year. Many large world-famous companies that occupy leading positions in the market are gradually introducing neural networks and artificial intelligence into the business management system.
The market for such technologies is already estimated at billions of dollars, and, according to experts, by 2025 it can grow 5 times or more. These technologies will reduce risks, classify data and enable the flow of information without human intervention, which reduces costs and increases profits many times over. At the same time, neural networks still require close control to avoid the accumulation of errors, but rich applications allow you to do this from remote devices.
