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Prediction of New Itinerary Markets for Airlines via Network Embedding
P. 315–325.
Kiselev D., Makarov I.
A large number of methods are being developed in the deep reinforcement learning area recently, but the scope of their application is limited. The number of environments does not always allow for a comprehensive assessment of a new agent training algorithm. The main purpose of this article is to present another environment for Match-3 game that could be expanded, which would have a connection with the real business. The results for the most popular deep reinforcement learning algorithms are presented as a baseline.
Ермолаев Е. С., Applied Network Science 2025 Vol. 10 Article 30
Recent advances in complex systems have highlighted the utility of simplicial complexes for modeling higher-order interactions, particularly in biological and physical networks. This study presents enhanced Simplex2Vec, an adaptation of the Simplex2Vec algorithm, to facilitate community detection within such structures. We compare enhanced Simplex2Vec’s efficacy against the Leiden algorithm and Spectral clustering using 7 distinct ...
Added: December 30, 2025
Gerasimova O., Syomochkina V., , in: Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected PapersVol. 12602.: Springer, 2021. P. 269–281.
Social networks are an integral part of modern life. They allow us to communicate online and exchange all kinds of information. In this paper, we consider the social network Instagram and its hashtags as a key tool for finding relevant information and new friends. The aim of our work is an empirical analysis of hashtags for posts in ...
Added: June 7, 2021
Makarov I., Makarov M., Kiselev D., PeerJ Computer Science 2021 Vol. 7 Article e526
Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, ...
Added: March 31, 2021
Makarov I., Kiselev D., Nikitinsky N. et al., PeerJ Computer Science 2021 Vol. 7 P. 1–62
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering in order to incorporate structural information into predictive model. Nowadays, a family of automated graph feature engineering techniques have been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs ...
Added: October 27, 2020
Anna Averchenkova, Alina Akhmetzyanova, Sudarikov K. et al., , in: Network Algorithms, Data Mining, and Applications. Springer Proceedings in Mathematics & Statistics.: Springer, 2020. P. 101–119.
Nowadays, a lot of scientists’ works aim to improve the quality of people’s life but it could be quite complicated without building a successful collaboration. Productive partnerships can increase research efficiency in many cases and make a huge impact on society. However, today there is no clear way to find such collaborators. In this paper, ...
Added: February 27, 2020
Makarov I., Gerasimova O., , in: Proceedings of the 14th International Workshop on Semantic and Social Media Adaptation and Personalization.: NY: IEEE, 2019. P. 1–6.
In this paper, we study the problem of predicting collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, in which authors play the role of nodes, and weighted edges connecting two authors are formed by storing either a number or quality metric of research papers co-authored ...
Added: July 30, 2019
Gerasimova O., Makarov I., , in: Advances in Computational Intelligence. IWANN 2019.: Berlin: Springer, 2019. P. 667–677.
In this paper, we study the problem of predicting quantity of collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, formed by authors writing papers in co-authorship represented by edges between authors in the network. Our task is formulated as regression for edge weights, for which ...
Added: July 29, 2019
Makarov I., Gerasimova O., Sulimov P. et al., PeerJ Computer Science 2019 P. 1–20
We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence ...
Added: January 21, 2019
Makarov I., Gerasimova O., Sulimov P. et al., , in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer ScienceVol. 11179.: Berlin: Springer, 2018. P. 20–31.
In this paper, we consider new formulation of graph embedding algorithm, while learning node and edge representation under common constraints. We evaluate our approach on link prediction problem for co-authorship network of HSE researchers’ publications. We compare it with existing structural network embeddings and feature-engineering models. ...
Added: September 5, 2018
Korolev S., Zhukov L. E., , in: "Информационные технологии и системы 2015" 39-я междисциплинарная школа-конференция 7 – 11 сентября, Олимпийская деревня, Сочи, Россия.: St. Petersburg: Институт проблем передачи информации им. А.А. Харкевича РАН, 2015. P. 1–8.
The problem of link prediction gathered a lot of attention in the last few years, arising in di erent applications ranging from recommendation systems to social networks. In this paper, we will describe the most popular similarity indices, compare their performance in their ability to show links with the highest probability of being removed from initial ...
Added: March 5, 2017