Network Algorithms, Data Mining, and Applications. Springer Proceedings in Mathematics & Statistics

Springer, 2020.

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Collaborator Recommender System

Anna Averchenkova, Alina Akhmetzyanova, Судариков К. В. и др., , 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, ...

Добавлено: 27 февраля 2020 г.

Network Analysis Methodology of Policy Actors Identification and Power Evaluation (the case of the Unified State Exam introduction in Russia)

Зайцев Д. Г., Gregory Khvatsky, Таловский Н. И. и др., , in: Network Algorithms, Data Mining, and Applications. Springer Proceedings in Mathematics & Statistics.: Springer, 2020. P. 231–244.

Добавлено: 7 ноября 2019 г.

GSM: Inductive Learning on Dynamic Graph Embeddings

Ананьева М. Е., Макаров И. А., Pendiukhov M., , in: Network Algorithms, Data Mining, and Applications. Springer Proceedings in Mathematics & Statistics.: Springer, 2020. P. 85–99.

In this paper, we study the problem of learning graph embeddings for dynamic networks and the ability to generalize to unseen nodes called inductive learning. Firstly, we overview the state-of-the-art methods and techniques for constructing graph embeddings and learning algorithms for both transductive and inductive approaches. Secondly, we propose an improved GSM based on GraphSAGE ...

Добавлено: 27 февраля 2020 г.