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Graph data representation is growing and becoming increasingly important because it helps solve complex problems involving large amounts of information. In this representation, data is structured in a way that reflects the relationships between different elements. This can be useful for social network analysis, web search, route optimization, and many other applications. One of the main reasons graph data is important is its ability to handle uncertainty and incompleteness of information. Unlike traditional relational databases, where data is stored in tables with a rigidly defined structure, graphs can contain information about different types of relationships between objects. This makes them ideal for working with data that is not always fully defined or has a complex structure. In addition, graph data representation also helps solve problems involving the analysis of large amounts of information. For example, when working with social networks, graphs can be used to identify groups of users with similar interests or behavior. Similarly, when searching the web, graphs can help determine the most relevant results based on the relationships between web pages. Thus, graph representation of data plays a key role in solving complex problems related to processing large amounts of information. It provides a flexible tool for data analysis and helps to find new patterns and relationships between different elements. Speaking about the applicability of graphs to bank data, we can say that the existing connections and relationships of bank clients can also be represented as a graph of client relationships. This approach to solving problems allows you to open a new perspective on solving classic problems of security departments in banks.
Keywords:graphs, big data, machine learning, data science, data science, deep learning.
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