3 papers
cs.LG2026
Graph Data Augmentation via Contrastive Generator Inversion ()
Mateusz Stolarski, Michał Czuba, Łukasz Kraiński +4
Graphs provide a natural representation of many complex systems, ranging from social platforms to ecosystems. However, the development of graph-based machine learning methods is of…
cs.SI2026
Twinning Complex Networked Systems: Data-Driven Calibration of the mABCD Synthetic Graph Generator
Piotr Bródka, Michał Czuba, Bogumił Kamiński +4
The increasing availability of relational data has contributed to a growing reliance on network-based representations of complex systems. Over time, these models have evolved to ca…
cs.SI2025
Multilayer Artificial Benchmark for Community Detection (mABCD)
Łukasz Kraiński, Michał Czuba, Piotr Bródka +3
One of the most persistent challenges in network science is the development of various synthetic graph models to support subsequent analyses. Among the most notable frameworks addr…