Prediction of retweet cascade size over time. - Andrey Kupavskii; Liudmila Ostroumova; Alexey Umnov; Svyatoslav Usachev; Pavel Serdyukov; Gleb Gusev; Andrey Kustarev (CIKM, 2012)

Empirical validation of the buckley-osthus model for the web host graph: degree and edge distributions. - Maxim Zhukovskiy; Dmitry Vinogradov; Yuri Pritykin; Liudmila Ostroumova; Evgeny Grechnikov; Gleb Gusev; Pavel Serdyukov; Andrei M. Raigorodskii (CIKM, 2012)

A Model of the Fresh Internet - Damien Lefortier; Liudmila Ostroumova; Egor Samosvat (CoRR, 2012)

Timely crawling of high-quality ephemeral new content. - Damien Lefortier; Liudmila Ostroumova; Egor Samosvat; Pavel Serdyukov (CIKM, 2013)

Studying page life patterns in dynamical web. - Alexey Tikhonov; Ivan Bogatyy; Pavel Burangulov; Liudmila Ostroumova; Vitaliy Koshelev; Gleb Gusev (SIGIR, 2013)

Evolution of the Media Web. - Damien Lefortier; Liudmila Ostroumova; Egor Samosvat (WAW, 2013)

Generalized Preferential Attachment: Tunable Power-Law Degree Distribution and Clustering Coefficient. - Liudmila Ostroumova; Alexander Ryabchenko; Egor Samosvat (WAW, 2013)

The Distribution of Second Degrees in the Buckley-Osthus Random Graph Model. - Andrey Kupavskii; Liudmila Ostroumova; Dmitry A. Shabanov; Prasad Tetali (Internet Math., 2013)

Crawling Policies Based on Web Page Popularity Prediction. - Liudmila Ostroumova; Ivan Bogatyy; Arseniy Chelnokov; Alexey Tikhonov; Gleb Gusev (ECIR, 2014)

Quick Detection of High-Degree Entities in Large Directed Networks. - Konstantin Avrachenkov; Nelly Litvak; Liudmila Ostroumova Prokhorenkova; Eugenia Suyargulova (ICDM, 2014)

Global Clustering Coefficient in Scale-Free Networks. - Liudmila Ostroumova Prokhorenkova; Egor Samosvat (WAW, 2014)

Adaptive Caching of Fresh Web Search Results. - Liudmila Ostroumova Prokhorenkova; Yury Ustinovskiy; Egor Samosvat; Damien Lefortier; Pavel Serdyukov (ECIR, 2015)

PageRank in Undirected Random Graphs. - Konstantin Avrachenkov; Arun Kadavankandy; Liudmila Ostroumova Prokhorenkova; Andrei M. Raigorodskii (WAW, 2015)

What can be Found on the Web and How: A Characterization of Web Browsing Patterns. - Alexey Tikhonov; Liudmila Ostroumova Prokhorenkova; Arseniy Chelnokov; Ivan Bogatyy; Gleb Gusev (WebSci, 2015)

Modularity of Complex Networks Models. - Liudmila Ostroumova Prokhorenkova; Pawel Pralat; Andrei M. Raigorodskii (WAW, 2016)

Publication Date Prediction through Reverse Engineering of the Web. - Liudmila Ostroumova Prokhorenkova; Petr Prokhorenkov; Egor Samosvat; Pavel Serdyukov (WSDM, 2016)

Recency-based preferential attachment models. - Liudmila Ostroumova Prokhorenkova; Egor Samosvat (J. Complex Networks, 2016)

Global Clustering Coefficient in Scale-Free Weighted and Unweighted Networks. - Liudmila Ostroumova Prokhorenkova (Internet Math., 2016)

Fighting biases with dynamic boosting. - Anna Veronika Dorogush; Andrey Gulin; Gleb Gusev; Nikita Kazeev; Liudmila Ostroumova Prokhorenkova; Aleksandr Vorobev (CoRR, 2017)

Modularity in several random graph models. - Liudmila Ostroumova Prokhorenkova; Pawel Pralat; Andrei M. Raigorodskii (Electron. Notes Discret. Math., 2017)

Local Clustering Coefficient in Generalized Preferential Attachment Models. - Alexander M. Krot; Liudmila Ostroumova Prokhorenkova (Internet Math., 2017)

Assortativity in Generalized Preferential Attachment Models. - Liudmila Ostroumova Prokhorenkova; Alexander M. Krot (Internet Math., 2017)

General results on preferential attachment and clustering coefficient. - Liudmila Ostroumova Prokhorenkova (Optim. Lett., 2017)

CatBoost: unbiased boosting with categorical features. - Liudmila Ostroumova Prokhorenkova; Gleb Gusev; Aleksandr Vorobev; Anna Veronika Dorogush; Andrey Gulin (NeurIPS, 2018)

Clustering Properties of Spatial Preferential Attachment Model. - Lenar Iskhakov; Bogumil Kaminski; Maksim Mironov; Pawel Pralat; Liudmila Ostroumova Prokhorenkova (WAW, 2018)

Preferential placement for community structure formation. - Liudmila Ostroumova Prokhorenkova; Egor Samosvat; Aleksandr Dorodnykh (Internet Math., 2018)

A Statistical Test of Heterogeneous Subgraph Densities to Assess Clusterability. - Pierre Miasnikof; Liudmila Prokhorenkova; Alexander Y. Shestopaloff; Andrei M. Raigorodskii (LION, 2019)

Using Synthetic Networks for Parameter Tuning in Community Detection. - Liudmila Prokhorenkova (WAW, 2019)

Community Detection through Likelihood Optimization: In Search of a Sound Model. - Liudmila Ostroumova Prokhorenkova; Alexey Tikhonov (WWW, 2019)

Learning Clusters through Information Diffusion. - Liudmila Ostroumova Prokhorenkova; Alexey Tikhonov; Nelly Litvak (WWW, 2019)

Graph-based Nearest Neighbor Search: From Practice to Theory. - Liudmila Prokhorenkova (CoRR, 2019)

Systematic Analysis of Cluster Similarity Indices: Towards Bias-free Cluster Validation. - Martijn Gösgens; Liudmila Prokhorenkova; Alexey Tikhonov (CoRR, 2019)

Embedding Words in Non-Vector Space with Unsupervised Graph Learning. - Max Ryabinin; Sergei Popov; Liudmila Prokhorenkova; Elena Voita (EMNLP (1), 2020)

Graph-based Nearest Neighbor Search: From Practice to Theory. - Liudmila Prokhorenkova; Aleksandr Shekhovtsov (ICML, 2020)

StochasticRank: Global Optimization of Scale-Free Discrete Functions. - Aleksei Ustimenko; Liudmila Prokhorenkova (ICML, 2020)

Global Graph Curvature. - Liudmila Prokhorenkova; Egor Samosvat; Pim van der Hoorn (WAW, 2020)

Overlaying Spaces and Practical Applicability of Complex Geometries. - Kirill Shevkunov; Liudmila Prokhorenkova (CoRR, 2020)

Boost then Convolve: Gradient Boosting Meets Graph Neural Networks. - Sergei Ivanov 004; Liudmila Prokhorenkova (ICLR, 2021)

Uncertainty in Gradient Boosting via Ensembles. - Andrey Malinin; Liudmila Prokhorenkova; Aleksei Ustimenko (ICLR, 2021)

Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures. - Martijn Gösgens; Alexey Tikhonov; Liudmila Prokhorenkova (ICML, 2021)

SGLB: Stochastic Gradient Langevin Boosting. - Aleksei Ustimenko; Liudmila Prokhorenkova (ICML, 2021)

Good Classification Measures and How to Find Them. - Martijn Gösgens; Anton Zhiyanov; Aleksey Tikhonov; Liudmila Prokhorenkova (NeurIPS, 2021)

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. - Andrey Malinin; Neil Band; Yarin Gal; Mark J. F. Gales; Alexander Ganshin; German Chesnokov; Alexey Noskov; Andrey Ploskonosov; Liudmila Prokhorenkova; Ivan Provilkov; Vatsal Raina; Vyas Raina; et al. (NeurIPS Datasets and Benchmarks, 2021)

Overlapping Spaces for Compact Graph Representations. - Kirill Shevkunov; Liudmila Prokhorenkova (NeurIPS, 2021)

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks. - Andrey Malinin; Neil Band; Alexander Ganshin; German Chesnokov; Yarin Gal; Mark J. F. Gales; Alexey Noskov; Andrey Ploskonosov; Liudmila Prokhorenkova; Ivan Provilkov; Vatsal Raina; Vyas Raina; et al. (CoRR, 2021)

Graph-based Nearest Neighbor Search in Hyperbolic Spaces. - Liudmila Prokhorenkova; Dmitry Baranchuk; Nikolay Bogachev; Yury Demidovich; Alexander Kolpakov (ICLR, 2022)

Good Classification Measures and How to Find Them. - Martijn Gösgens; Anton Zhiyanov; Alexey Tikhonov; Liudmila Prokhorenkova (CoRR, 2022)

Gradient Boosting Performs Low-Rank Gaussian Process Inference. - Aleksei Ustimenko; Artem Beliakov; Liudmila Prokhorenkova (CoRR, 2022)

Characterizing Graph Datasets for Node Classification: Beyond Homophily-Heterophily Dichotomy. - Oleg Platonov; Denis Kuznedelev; Artem Babenko; Liudmila Prokhorenkova (CoRR, 2022)

When Less Is More: Systematic Analysis of Cascade-Based Community Detection. - Liudmila Prokhorenkova; Alexey Tikhonov; Nelly Litvak (ACM Trans. Knowl. Discov. Data, 2022)

A critical look at the evaluation of GNNs under heterophily: Are we really making progress? - Oleg Platonov; Denis Kuznedelev; Michael Diskin; Artem Babenko; Liudmila Prokhorenkova (ICLR, 2023)

Gradient Boosting Performs Gaussian Process Inference. - Aleksei Ustimenko; Artem Beliakov; Liudmila Prokhorenkova (ICLR, 2023)

Which Tricks are Important for Learning to Rank? - Ivan Lyzhin; Aleksei Ustimenko; Andrey Gulin; Liudmila Prokhorenkova (ICML, 2023)

Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts. - Gleb Bazhenov; Denis Kuznedelev; Andrey Malinin; Artem Babenko; Liudmila Prokhorenkova (NeurIPS, 2023)

Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond. - Oleg Platonov; Denis Kuznedelev; Artem Babenko; Liudmila Prokhorenkova (NeurIPS, 2023)

Neural Algorithmic Reasoning Without Intermediate Supervision. - Gleb Rodionov; Liudmila Prokhorenkova (NeurIPS, 2023)

Revisiting Graph Homophily Measures. - Mikhail Mironov; Liudmila Prokhorenkova (LoG, 2024)

Challenges of Generating Structurally Diverse Graphs. - Fedor Velikonivtsev; Mikhail Mironov; Liudmila Prokhorenkova (NeurIPS, 2024)

Discrete Neural Algorithmic Reasoning. - Gleb Rodionov; Liudmila Prokhorenkova (CoRR, 2024)

TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features. - Gleb Bazhenov; Oleg Platonov; Liudmila Prokhorenkova (CoRR, 2024)

Measuring Diversity: Axioms and Challenges. - Mikhail Mironov; Liudmila Prokhorenkova (CoRR, 2024)

Evaluating Graph Generative Models with Graph Kernels: What Structural Characteristics Are Captured? - Martijn Gösgens; Alexey Tikhonov; Liudmila Prokhorenkova (Trans. Mach. Learn. Res., 2024)