3 papers
cs.LG2026
Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models
Harry Proshian, Nikita Severin, Sergey Nikolenko +5
Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recomm…
cs.LG2025
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
Kirill Lukyanov, Mikhail Drobyshevskiy, Georgii Sazonov +2
The growing need for Trusted AI (TAI) highlights the importance of interpretability and robustness in machine learning models. However, many existing tools overlook graph data and…
cs.LG2025
Robustness questions the interpretability of graph neural networks: what to do?
Kirill Lukyanov, Georgii Sazonov, Serafim Boyarsky +1
Graph Neural Networks (GNNs) have become a cornerstone in graph-based data analysis, with applications in diverse domains such as bioinformatics, social networks, and recommendatio…