58 citations · 60 across the 3 of their papers we have counts for
5 papers
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
Kirill Lukyanov, Mikhail Drobyshevskiy, Georgii Sazonov +2
The growing need for Trusted AI (TAI) highlights the importance of interpretability and robustness in machine learning models. However, many existing tools overlook graph data and…
Random Graph Modeling: A survey of the concepts
Mikhail Drobyshevskiy, Denis Turdakov
Random graph (RG) models play a central role in the complex networks analysis. They help to understand, control, and predict phenomena occurring, for instance, in social networks,…
Collecting Influencers: A Comparative Study of Online Network Crawlers
Mikhail Drobyshevskiy, Denis Aivazov, Denis Turdakov +3
Online network crawling tasks require a lot of efforts for the researchers to collect the data. One of them is identification of important nodes, which has many applications starti…
Graph Neural Network for Crawling Target Nodes in Social Networks
Kirill Lukyanov, Mikhail Drobyshevskiy, Danil Shaikhelislamov +1
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 c…
Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process
Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov +2
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of th…