Complex Networks Analysis

Complex Networks Analysis is the methodological core of the team’s work: the community detection, temporal analysis, and benchmarking methods developed here support the team’s more applied research directions.

On the methods side, the team develops and benchmarks community detection algorithms, anomaly and change point detection in temporal interactions, and comparative studies of detection methods. Ongoing PhD work extends this line to change point detection directly in linkstreams (continuous interaction sequences, as opposed to time-sliced snapshots).

Applied to the evolution of communities over time, this work covers the enumeration of temporal motifs describing community evolution, and the visualization of community evolution using Sankey diagrams. A related research direction investigates how large online collectives, such as Wikipedia or open-source software communities, grow and structure themselves over time, looking for invariant mechanisms behind their transitions between phases of low and high activity, and how individual contributors’ roles and trajectories relate to that collective-level structuring.