The widespread use of social media platforms has led to the accumulation of vast amounts of stored data, enabling the prediction of rare events based on user interaction analysis. This study presents a method for predicting rare events using graph theory, particularly graphlets. The social network VKontakte, with over 90 million users, serves as the data source. The ORCA algorithm is utilized to identify characteristic graph structures within the data. Throughout the study, user interactions were analyzed to identify precursors of rare events and assess prediction accuracy. The results demonstrate the effectiveness of the proposed method, its potential for threat monitoring, and the possibilities for further refinement of graphlet-based prediction models.

Keywords: social media, security event, event prediction, graph theory, graphlet, interaction analysis, time series analysis, correlation analysis, data processing, anomalous activity

Based on the analysis of behavioral characteristics, the main indicators that provide the greatest accuracy in identifying users of mobile devices are identified. As part of the research, software has been written to collect touchscreen data when performing typical user actions. Identification algorithms are implemented based on machine learning algorithms and accuracy is shown. The results obtained in the study can be used to build continuous identification systems.

Keywords: user behavior, touch screen, continuous identification, biometrics, dataset, classification, deep learning, recurrent neural network, mobile device