Community Dynamics & Individual Trajectories

This work analyzes and tracks how communities in temporal networks evolve, combining algorithm choice and benchmarking with several current and upcoming directions.

The team compares and benchmarks community detection algorithms for their ability to track how communities evolve, including a benchmarking framework built on customizable, realistic ground truths, and visualizes community evolution through purpose-built diagrams. These methods have been applied to real platforms, from crowdfunding communities to community dynamics and echo chambers on YouTube.

One current direction extends change point detection directly within linkstreams (continuous sequences of interactions, rather than time-sliced snapshots), to distinguish a lasting shift in a community from ordinary fluctuation without the sampling bias snapshots can introduce.

Another current direction looks at building domain-relevant metrics into community detection and clustering algorithms themselves, for example by weighting interactions or choosing filtering criteria that reflect what actually matters for a given application, explored on large real-world graphs such as Wikipedia’s.

Looking ahead, an upcoming PhD track will extend this work to individual trajectories: how members join, contribute, and move between communities over time, tracing each person’s evolving position within the community structures these methods detect.