| Survey on graph embeddings and their applications to machine learning problems on graphs I Makarov, D Kiselev, N Nikitinsky, L Subelj PeerJ Computer Science 7, e357, 2021 | 112 | 2021 |
| Fusion of text and graph information for machine learning problems on networks I Makarov, M Makarov, D Kiselev PeerJ Computer Science 7, e526, 2021 | 31 | 2021 |
| Jonnee: Joint network nodes and edges embedding I Makarov, K Korovina, D Kiselev IEEE Access 9, 144646-144659, 2021 | 29 | 2021 |
| Temporal network embedding framework with causal anonymous walks representations I Makarov, A Savchenko, A Korovko, L Sherstyuk, N Severin, D Kiselev, ... PeerJ Computer Science 8, e858, 2022 | 25 | 2022 |
| Ti-DC-GNN: Incorporating time-interval dual graphs for recommender systems N Severin, A Savchenko, D Kiselev, M Ivanova, I Kireev, I Makarov Proceedings of the 17th ACM Conference on Recommender Systems, 919-925, 2023 | 6 | 2023 |
| Prediction of new itinerary markets for airlines via network embedding D Kiselev, I Makarov International Conference on Analysis of Images, Social Networks and Texts …, 2019 | 4 | 2019 |
| Predicting Molecule Toxicity via Descriptor-Based Graph Self-Supervised Learning X Li, I Makarov, D Kiselev IEEE Access, 2023 | 2 | 2023 |
| Exploration in Sequential Recommender Systems via Graph Representations D Kiselev, I Makarov IEEE Access 10, 123614-123621, 2022 | 2 | 2022 |
| LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering N Severin, A Ziablitsev, Y Savelyeva, V Tashchilin, I Bulychev, M Yushkov, ... arXiv preprint arXiv:2411.00556, 2024 | | 2024 |
