Evolutionary graph theory solves the longstanding puzzle of why diverse infectious diseases and cancers show similar (approximately lognormal) distributions of their incubation periods.

Analysis of resting-state fMRI data revealed nonrandom temporal transitions between spontaneous brain activity patterns in both awake rats and humans.

A simple model reveals the roles of colony geometry and diffusion rate in allowing microbes to cooperate by sharing public goods.

Multiscale Graph Correlation, an interpretable hypothesis test with strong theoretical guarantees for discerning relationships in complex data, requires about half the sample size as other methods, whilst maintaining computational tractability.

A realistic model of the connections between local populations of neurons in the adult mouse brain can be constructed based on just two biologically plausible rules.

WaveMAP is a novel approach that combines nonlinear dimensionality reduction with graph clustering on extracellular waveforms to reveal previously obscured cell type diversity in monkey cortex.

Temporally delayed linear modelling provides a domain-general linear framework for sequence detection and statistical testing, and is able to detect replays in both human neuroimaging and animal electrophysiology.

Analysis of conformational heterogeneity in cryo-EM data by a recently developed deep learning method reveals the structure and dynamics of the oncogenic chromatin remodeler ALC1 bound to a poly-ADP-ribosylated nucleosome.

Graph analysis revealed that spontaneous fluctuations of episodic-memory encoding performance associate with time-varying brain connectivity networks.

A network-based ‘speed dating’ approach can promote interactions between delegates at scientific meetings.