Flow stability for dynamic community detection
arXiv:2101.06131 · doi:10.1126/sciadv.abj3063
Abstract
Many systems exhibit complex temporal dynamics due to the presence of different processes taking place simultaneously. An important task in such systems is to extract a simplified view of their time-dependent network of interactions. Community detection in temporal networks usually relies on aggregation over time windows or consider sequences of different stationary epochs. For dynamics-based methods, attempts to generalize static-network methodologies also face the fundamental difficulty that a stationary state of the dynamics does not always exist. Here, we derive a method based on a dynamical process evolving on the temporal network. Our method allows dynamics that do not reach a steady state and uncovers two sets of communities for a given time interval that accounts for the ordering of edges in forward and backward time. We show that our method provides a natural way to disentangle the different dynamical scales present in a system with synthetic and real-world examples.
54 pages, 14 figures. Accepted version by Science Advances
References in corpus (12)
- Fast unfolding of communities in large networks
- Modularity and community structure in networks
- Finding community structure in networks using the eigenvectors of matrices
- Maps of random walks on complex networks reveal community structure
- Resolution limit in community detection
- Quantifying social group evolution
- Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems
- Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks
- Size reduction of complex networks preserving modularity
- Stream Graphs and Link Streams for the Modeling of Interactions over Time
- Temporal stability of network partitions
- A time resolved clustering method revealing longterm structures and their short-term internal dynamics