Векторные модели графов в задачах машинного обучения на структурных данныхGraph embeddings for machine learning on structural data

The thesis investigates graph embeddings for machine learning on structural data. It highlights critical privacy vulnerabilities in static graph neural networks (GNNs), showing that training links can be extracted through membership inference attacks. To mitigate these risks and better reflect the evolving nature of real-world systems, the research transitions to dynamic graphs. A unified benchmarking framework is introduced to enable fair evaluation of dynamic GNNs, addressing inconsistencies in prior studies. Based on this framework, the re-evaluation of leading models has uncovered new insights into their performance. Finally, the thesis develops two novel edge-centric architectures that directly model temporal dependencies through interaction patterns, achieving superior performance by capturing fine-grained dynamics often missed by node-centric approaches.