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Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
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Ivanov S., Prokhorenkova L.
Language:
English
Arteaga Moreano B. D., Chervov N., Poptsova M., Scientific Reports 2026 Vol. 16 No. 1 Article 4772
Accurate prediction of protein-protein interactions (PPIs) is fundamental to understanding biological processes and disease mechanisms. While deep learning offers a powerful alternative to costly experimental methods, existing approaches often overlook critical protein-surface information and rely on simplistic feature fusion techniques, thereby limiting performance. To address this, we introduce GSMFormer-PPI, a novel multimodal framework that integrates ...
Added: February 4, 2026
Ivanov S., Borisov V., Ali S. et al., , in: 2025 IEEE XVII International Scientific and Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE).: IEEE, 2025. Ch. 127 P. 1–7.
This paper investigates the problem of detecting slow refrigerant leaks in a data center cooling system using a graph neural network. The study addresses the challenge of early fault identification, proposing a method for constructing a topological graph based on the engineering diagram, the physical layout, and the cause-and-effect relationships in the cooling system. This ...
Added: December 19, 2025
Yusupov V., Rakhuba M., Frolov E., , in: CIKM '25: Proceedings of the 34rd ACM International Conference on Information and Knowledge Management.: ACM, 2025. Ch. 1 P. 5469–5473.
In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely important to maintain the relevance of the recommendations under the conditions of a large amount of new data and changing user preferences. To address this issue, we adapt the ...
Added: October 3, 2025
Cham: Springer, 2025.
This book constitutes the refereed proceedings of 34th International Workshops which were held in conjunction with the 34th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2025, held in Kaunas, Lithuania, September 9–12, 2025.
The 20 full papers and 8 abstracts included in this workshop volume were carefully reviewed and selected from 42 submissions. ...
Added: September 29, 2025
Danil Shaikhelislamov, Denis Turdakov, , in: 2022 Ivannikov Ispras Open Conference (ISPRAS).: IEEE, 2022. P. 31–36.
Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper ...
Added: February 3, 2025
D. Shaikhelislamov, Lukyanov K., Severin N. et al., Journal of Mathematical Sciences 2024 Vol. 285 P. 234–244
Graph neural networks (GNNs) have shown great promise in a variety of tasks involving graph data, including recommendation systems. However, as GNNs become more widely adopted in practical applications, concerns have arisen about their vulnerability to adversarial attacks. These attacks can lead to biased recommendations, potentially causing economic losses and safety risks. In this work, ...
Added: February 3, 2025
Fomin D., Ilya Makarov, Voronina M. et al., IEEE Access 2024 Vol. 12 P. 196195–196206
Efficient task scheduling and resource allocation in manufacturing are vital for gaining competitive advantages in dynamic economic environments. Modern manufacturing systems must integrate logistics considerations such as delivery times and costs, yet traditional scheduling methods often overlook these factors. To address this gap, we investigate task scheduling in cloud manufacturing systems, emphasizing logistics integration. We ...
Added: January 19, 2025
Bocharnikov V., Derkach D., Ratnikov F., , in: Physics of Particles and Nuclei.: [б.и.], 2024. P. 995–999.
Added: November 21, 2024
Zelenkov Y., , in: 2023 Ivannikov ISPRAS Open Conference (ISPRAS).: IEEE, 2023. P. 176–182.
We propose a regression ensemble based on a decomposition that separates the weighted average errors of individual learners and the ambiguity of their estimates. This approach is a modification of Gradient Boosting with a variation of the gradient at each step. That allows ensuring explicitly a diversity of base estimators. In addition, the proposed approach ...
Added: May 1, 2024
Bukina T. V., Kashin D., Экономический журнал Высшей школы экономики 2024 Т. 28 № 1 С. 81–107
The paper reveals the forecasts for regional inflation based on the regions of the Privolzhskiy Federal District (PFD). The purpose of the study is to determine the model that most accurately predicts regional inflation. The paper compares the tools of machine learning – support vector machines, gradient boosting, and random forest – with econometric models ...
Added: February 13, 2024
Bazhenov G., Kuznedelev D., Malinin A. et al., , in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023).: Curran Associates, Inc., 2023. P. 75567–75594.
In reliable decision-making systems based on machine learning, models have to be robust to distributional shifts or provide the uncertainty of their predictions. In node-level problems of graph learning, distributional shifts can be especially complex since the samples are interdependent. To evaluate the performance of graph models, it is important to test them on diverse ...
Added: February 7, 2024
Lukin R., Grigoriev R., Yarullin A. et al., , in: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track.: PMLR, 2022. P. 1–12.
In this report, we describe the Open Catalyst Challenge held at NeurIPS 2021, focusing on using machine learning (ML) to accelerate the search for low-cost catalysts that can drive reactions converting renewable energy to storable forms. Specifically, the challenge required participants to develop ML approaches for relaxed energy prediction, i.e. given atomic positions for an ...
Added: January 12, 2024
Gerasimova O., Makarov I., Severin N., IEEE Access 2023 Vol. 11 P. 88074–88086
The problem of query answering over incomplete attributed graph data is a challenging field of database management systems and artificial intelligence. When there are rules on data structure expressed in the form of the ontology, the theoretical complexity of finding exact solution satisfying ontology constraints increases. Logic-based methods use theoretical constructions to obtain efficient rewritings ...
Added: January 5, 2024
[б.и.], 2022.
The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore the detection task. In this work, we consider this problem from the uncertainty estimation perspective and perform the comparison of several recently proposed methods. In our experiment, we find ...
Added: December 12, 2023
Gerasyov Matvey, Makarov I., IEEE Access 2023 Vol. 11 P. 89180–89187
Deep reinforcement learning in partially observable environments is a difficult task in itself and can be further complicated by a sparse reward signal. Most tasks involving navigation in three-dimensional environments provide the agent with minimal information. Typically, the agent receives a visual observation input from the environment and is rewarded once at the end of ...
Added: August 28, 2023
Park C., Lee C., Bahng H. et al., CIKM: ACM International Conference on Information & Knowledge Management (США) 2020 P. 1215–1224
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel spatio-temporal graph attention (ST-GRAT) that effectively captures the spatio-temporal dynamics ...
Added: May 18, 2023
Muratova A., Mitrofanova E., Islam R., , in: Procedia Computer Science: 11th International Young Scientist Conference on Computational ScienceVol. 212.: Elsevier, 2022. P. 358–367.
The article presents a case study on demographic sequences analysis through modern machine learning (ML)
techniques. The studied data contains demographic and socioeconomic events, where the events are presented
as sequences of statuses. The involved demographers are interested in applications of advanced ML techniques
and interpretable patterns for their needs. We show how Shapley value-based explanations can be ...
Added: September 10, 2022
Kiselev D., Makarov I., IEEE Access 2022 Vol. 10 P. 123614–123621
Temporal graph networks are powerful tools for solving the cold-start problem in sequential recommender systems. However, graph models are susceptible to feedback loops and data distribution shifts. The paper proposes a simple yet efficient graph-based exploration method for the mitigation of the issues above. It adopts the counter-based state exploration from reinforcement learning to the ...
Added: September 5, 2022
С. М. Авдошин, Г. А. Арутюнов, Информационные технологии 2022 Т. 28 № 7 С. 378–391
The global pandemic has outlined the shortfall of human resources in the information technology sector. On the estimation of analysts, the labor shortage of IT-specialists in Russia in 2021 is between 500 thousand and 1 million people. Educating and bringing to market such numerous personnel may take years. The task of optimizing the process of ...
Added: June 11, 2022