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.

Contents:

• Introduction
  • Installation/Usage
• Generating time series of spiking neurons
  • Planting dynamic communities into synthetic time series data
  • Constructing chronologically ordered set of network states
• Dynamic Community Detection (DCD)
  • Skeleton coupling
  • Computing partition quality
• The temporal_network class
  • temporal_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()

Indices and tables

• Index

• Module Index

• Search Page