3 papers
cs.LG2026
A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches
Minh H. Vu, Daniel Edler, Carl Wibom +3
Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current…
physics.soc-ph2026
Community Detection with the Map Equation and Infomap: Theory and Applications
Jelena SmiljaniÄ, Christopher Blöcker, Anton Holmgren +3
Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and…
physics.soc-ph2025
Mapping memory-biased dynamics with compact models reveals overlapping communities in large networks
Maja Lindström, Rohit Sahasrabuddhe, Anton Holmgren +3
Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional or…