DSA Faculty
API
← к списку преподавателей

Баженов Глеб Владимирович

Факультет компьютерных наук

Профиль на hse.ru ↗ тел.: 28841
Публикаций
3
Языков
2
Наград
1
Конференций
0
Профиль Публикации (3) Курсы (2)

Профессиональные интересы

graph machine learning

Должности

  • Приглашенный преподаватель — Факультет компьютерных наук, Департамент больших данных и информационного поиска, Базовая кафедра Яндекс
  • Младший научный сотрудник — Факультет компьютерных наук, Департамент больших данных и информационного поиска, Научно-учебная лаборатория компании Яндекс
  • Аспирант — Факультет компьютерных наук, Департамент больших данных и информационного поиска, Научно-учебная лаборатория компании Яндекс

Био

  • · Начал работать в НИУ ВШЭ в 2021 году.

Образование

  • 2023 · Магистратура: Сколковский институт науки и технологий, специальность «Математика и компьютерные науки», квалификация «Магистр»
  • 2021 · Бакалавриат: Национальный исследовательский университет "Высшая школа экономики", специальность «Прикладная математика», квалификация «Бакалавр»

Опыт работы

  • · 2022: Machine Learning Researcher at Yandex Research, april present
  • · 2021: Machine Learning Engineer at Sber, april august

Награды и поощрения

  • · Надбавка за публикацию в журнале из Списка А (и приравненном к нему научном издании) (2025–2026, 2024–2025)

Идентификаторы исследователя

Публикации (3)

GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data

2025 · CHAPTER · en

Although data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML benchmarks for node property prediction only cover a surprisingly narrow set of data domains, and graph neural networks (GNNs) are often evaluated on just a few academic citation networks. This issue is particularly pressing in light of the recent growing interest in designing graph foundation models. These models are supposed to be able to transfer to diverse graph datasets from different domains, and yet the proposed graph foundation models are often evaluated on a very limited set of datasets from narrow applications. To alleviate this issue, we introduce GraphLand: a benchmark of 14 diverse graph datasets for node property prediction from a range of different industrial applications. GraphLand allows evaluating graph ML models on a wide range of graphs with diverse sizes, structural characteristics, and feature sets, all in a unified setting. Further, GraphLand allows investigating such previously underexplored research questions as how realistic temporal distributional shifts under transductive and inductive settings influence graph ML model performance. To mimic realistic industrial settings, we use GraphLand to compare GNNs with gradient-boosted decision trees (GBDT) models that are popular in industrial applications and show that GBDTs provided with additional graph-based input features can sometimes be very strong baselines. Further, we evaluate currently available general-purpose graph foundation models and find that they fail to produce competitive results on our proposed datasets.

DOI ↗

Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts

2023 · CHAPTER · en

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 and meaningful distributional shifts. However, most graph benchmarks considering distributional shifts for node-level problems focus mainly on node features, while structural properties are also essential for graph problems. In this work, we propose a general approach for inducing diverse distributional shifts based on graph structure. We use this approach to create data splits according to several structural node properties: popularity, locality, and density. In our experiments, we thoroughly evaluate the proposed distributional shifts and show that they can be quite challenging for existing graph models. We also reveal that simple models often outperform more sophisticated methods on the considered structural shifts. Finally, our experiments provide evidence that there is a trade-off between the quality of learned representations for the base classification task under structural distributional shift and the ability to separate the nodes from different distributions using these representations.

Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective

2022 · CHAPTER · en

-

DOI ↗

Курсы (2)