Welcome to Community Characterization in Temporal Networks’s documentation!¶
Community Characterization in Temporal Networks is our python wrap-up for applying dynamic community detection on temporal networks obtained from spike-train data. We generate spiking neuronal activity with varying community events and compare the performances of 5 different community detection algorithms: MMM, Infomap, Tensor Factorization, DSBM and DPPM where DPPM is not included in this package since it is available in MATLAB.
- Introduction
- Generating time series of spiking neurons
- Dynamic Community Detection (DCD)
- The
temporal_network
classtemporal_network
bin_time_series()
binarize()
community_consensus_iterative()
consensus_display()
create_time_series()
cross_correlation_matrix()
display_truth()
find_repeated()
gaussian_filter()
generate_ground_truth()
generate_transient()
getOverlap()
get_repeated_indices()
information_recovery()
jitter()
max_norm_cross_corr()
normalized_cross_corr()
space_comms()
spike_count()
threshold()