52 citations · 66 across the 5 of their papers we have counts for
5 papers
Reliable data clustering with Bayesian community detection
Magnus Neuman, Jelena Smiljanić, Martin Rosvall
From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Most methods work in two steps: first re…
Community Detection with the Map Equation and Infomap: Theory and Applications
Jelena Smiljanić, 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…
Cross-validation of correlation networks using modular structure
Magnus Neuman, Viktor Jonsson, Joaquín Calatayud +1
Correlation networks derived from multivariate data appear in many applications across the sciences. These networks are usually dense and require sparsification to detect meaningfu…
Module-based regularization improves Gaussian graphical models when observing noisy data
Magnus Neuman, Joaquín Calatayud, Viktor Tasselius +1
Inferring relations from correlational data allows researchers across the sciences to uncover complex connections between variables for insights into the underlying mechanisms. The…
Exploring the solution landscape enables more reliable network community detection
Joaquín Calatayud, Rubén Bernardo-Madrid, Magnus Neuman +2
To understand how a complex system is organized and functions, researchers often identify communities in the system's network of interactions. Because it is practically impossible…