52 citations · 52 across the 2 of their papers we have counts for
7 papers
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann +2
A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if…
Group Centrality Maximization for Large-scale Graphs
Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski +3
The study of vertex centrality measures is a key aspect of network analysis. Naturally, such centrality measures have been generalized to groups of vertices; for popular measures i…
Certifiable Robustness to Graph Perturbations
Aleksandar Bojchevski, Stephan Günnemann
Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness. This is even more alarming given recent findings showin…
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski +1
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on…
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski, Stephan Günnemann
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despi…
Dual-Primal Graph Convolutional Networks
Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski +3
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Grap…