A method for semantic graph analysis of the evolution of internet fraud is proposed based on the construction and comparative analysis of temporal semantic networks. The study is conducted using a corpus of 51,755 thematic documents retrieved from the InfoStream information system covering the period 2011–2025. A Retrieval-Augmented Generation (RAG)-based workflow was employed to retrieve relevant document contexts and support their semantic processing during network construction. Temporal semantic networks representing the conceptual structure of internet fraud at successive stages of its evolution were constructed for three five-year intervals, together with an integral semantic network characterizing the stable conceptual core of the subject domain.
The topological characteristics of the networks were analyzed using the framework of graph theory—specifically degree, betweenness, and eigenvector centrality—enabling the quantitative assessment of structural changes, the identification of concepts with a high transformational role, and the tracking of thematic cluster evolution. It is demonstrated that the development of internet fraud is predominantly evolutionary in nature, occurring through the restructuring of existing semantic structures rather than their complete replacement.
Building upon this foundation, a hypothesis of structural continuity for semantic networks is formulated, and a model for forecasting their evolution is proposed. This model involves estimating the structural transformation operator, predicting the emergence of new concepts and semantic relationships, and constructing the forecasted network for the subsequent time interval. The validity of the proposed approach is confirmed through retrospective validation, which entails forecasting semantic networks for the subsequent period using solely data from preceding temporal slices, followed by a comparison between the forecasted and actual structures. The obtained results demonstrate the potential of utilizing temporal semantic-graph analysis as a tool for the early detection of structural changes in the information space and the forecasting of new trends in the development of internet fraud. The retrospective validation confirmed the model's high predictive capability, achieving a Precision of 0.870, a Recall of 0.816, and an F1-score of 0.842 for the 2021–2025 forecast interval, alongside a strong rank correlation (ρ = 0.811) in preserving node centrality. These quantitative results demonstrate the practical validity of temporal semantic-graph analysis as a tool for the early detection of structural changes and the forecasting of new trends in the development of internet fraud.