Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach

doi:10.1371/journal.pone.0086028

Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach

Figure 6

Activity patterns of the extracted components.

Each panel corresponds to one component obtained by non-negative tensor factorization of the school temporal network, with , and provides the activity level of the component as a function of the time of the day. For clarity, the panels only show the activity patterns for the first day of data (see Fig. S for the second day). Components that can be matched to classes are marked as class. The other three components that correspond to mixed classes exhibit activity patterns that can be understood in terms of gatherings in the social spaces of the school.

doi: https://doi.org/10.1371/journal.pone.0086028.g006