1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+)
Jordan Barrett, Ryan DeWolfe, Bogumił Kamiński +3
The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The…
Improving community detection via community association strength scores
Jordan Barrett, Ryan DeWolfe, Bogumił Kamiński +3
Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms ge…