2 citations · 2 across the 2 of their papers we have counts for
7 papers
Constrained user-item allocation for e-commerce marketing campaigns
Maja Lindström, Natalija Glisovic, Jan von Pichowski +2
When running marketing campaigns, retailers must decide which products to promote and which users to target. These decisions are inherently coupled: effective campaigns match users…
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…
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…
Deep Graph Learning will stall without Network Science
Christopher Blöcker, Martin Rosvall, Ingo Scholtes +1
Deep graph learning focuses on flexible and generalizable models that learn patterns in an automated fashion. Network science focuses on models and measures revealing the organizat…
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…
Compressing regularized dynamics improves link prediction with the map equation in sparse networks
Maja Lindström, Christopher Blöcker, Tommy Löfstedt +1
Predicting future interactions or novel links in networks is an indispensable tool across diverse domains, including genetic research, online social networks, and recommendation sy…