Dynamic community detection decouples hierarchical timescale behavior of complex chemical systems

Although community or cluster identification is becoming a standard tool within the simulation community, traditional algorithms are challenging to adapt to time dependent data. Here we introduce temporal community identification using the delta-screening algorithm which has the flexibility to account for varying community compositions, merging and splitting behaviors within dynamically evolving chemical networks. When applied to a complex chemical system whose varying chemical environments cause multiple timescale behavior, delta-screening is able to resolve the hierarchical timescales of temporal communities. This computationally efficient algorithm is easily adapted to a wide range of dynamic chemical systems; flexibility in implementation allows the user to increase or decrease the resolution of temporal features by controlling parameters associated with community composition and fluctuations therein.