Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
PB
GC
TS Journal of Statistical PhysicsInformation asymmetry
Network users who have access to the network’s most informative node, as quantified by a novel index, the InfoRank, have a competitive edge.
PB
FLPMDT Journal of Statistical MechanicsDisentangling links in networks
Inference from single snapshots of temporal networks can misleadingly group communities if the links between snapshots are correlated.
JPCTGC
Physical Review ECommunities in networks
A new tool derived from information theory quantitatively identifies trees, hierarchies and community structures within complex networks.
AAAC
NP Journal of the Royal Society InterfaceProtein interaction experiments
Properties of protein interaction networks test the reliability of data and hint at the underlying mechanism with which proteins recruit each other.
Random graphs with short loops
The analysis of real networks which contain many short loops requires novel methods, because they break the assumptions of tree-like models.
Self-healing complex networks
The interplay between redundancies and smart reconfiguration protocols can improve the resilience of networked infrastructures to failures.
Reconstructing credit
New mathematical tools can help infer financial networks from partial data to understand the propagation of distress through the network.
Weighted network evolution
A statistical procedure identifies dominant edges within weighted networks to determine whether a network has reached its steady state.
Metric for fitness and complexity
A quantitative assessment of the non-monetary advantage of diversification represents a country’s hidden potential for development and growth.
Clustering inverted
Edge multiplicity—the number of triangles attached to edges—is a powerful analytic tool to understand and generalize network properties.