Graph technologies in an intelligent system of complex analysis of data from Internet sources

The purpose of the work outlined in the article is to review and demonstrate the use of graph technologies for deep data analysis. The first part of the article discusses the Intelligent System for the Comprehensive Analysis of Internet Sources Data and its possible directions for its further development. This system is a multi-purpose cluster using technologies for constructing a knowledge graph, methods and models of machine learning for in-depth analysis of data from Internet sources (for example, scientific publications, social networks, media). The purpose of the analysis is to identify the most important publications in a certain area (for example, in robotics, space research, healthcare, in the social sphere), thematic analysis of these publications, to identify the leader of a scientific direction and to predict trends in the development of directions and interaction of groups of people. When developing this system, we utilized probabilistic machine learning algorithms and methods for constructing and maintaining a graph model of the social network of authors and their publications, determining the rating of a particular author, determining the topics of publications and classifying them by areas of knowledge. The basis for the creation of intelligent applications is graph technology, which allows you to make predictions that are more accurate. The combined application of methods and algorithms of machine learning with graph technologies allows you to get hidden dependencies and perform predictive analysis of information, get answers in real time, and implement artificial intelligence algorithms. Methods of collaboration with graph technologies and a learning machine (for example, using neural networks) are based on graph embedding. This technology allows you to perform a comprehensive, deep and intelligent analysis of information. At the end of the article, there are analytical reports obtained using graph technologies in the Intelligent System for Complex Analysis of Internet Sources Data.

1. Diestel R. Graph Theory. Berlin: Springer-Verlag; 2017.

2. Needham M., Hodler Amy E. Graph Algorithms. Sebastopol: O’Reilly Media; 2019.

3. Hamilton W.L., Rex Ying, Leskovec J. Representation Learning on Graphs: Methods and Applications. Stanford: Stanford University; 2017; 9:1-25.