Skip to content

Latest commit

 

History

History
94 lines (83 loc) · 6.45 KB

README.md

File metadata and controls

94 lines (83 loc) · 6.45 KB
Raw

communityalg

Algorithms and functions in Matlab for community detection in networks. Expands BrainConnectivity toolbox.

Matlab/Octave algorithms and functions

  • ami.m Returns the adjusted mutual information between two membership vectors.
  • association_score.m Returns the association score between pairs of communities specified by the graph and membership.
  • asymptotic_modularity.m Compute asymptotic modularity of a graph with respect to a membership vector.
  • asymptotic_modularity_sum.m TODO
  • asymptotic_surprise.m Compute asymptotic surprise of a graph with respect to a membership vector.
  • clique.m Generate an adjacency matrix of a clique graph with n nodes.
  • cluster_similarity.m Compare two membership vectors.
  • clustering_entropy.m Compute the clustering entropy of an agreement matrix as in "Gfeller, Newman, 2006".
  • community_robustness_weighted.m TODO
  • community_size2memb.m Convert an array where every element is the size of a clique to the correspondig membership vector.
  • comm_mat.m Returns the block matrix of a graph and its community structure as membership.
  • compute_surprise.m Compute the surprise given surprise paramenters.
  • consensus_clustering.m TODO
  • consensus_clustering_weighted.m TODO
  • consensus_entropy.m TODO
  • consensus_robustness.m TODO
  • correlation_louvain.m Adaptation of the BCT community_louvain method for correlation matrices, as described in MacMahon,2015.
  • count_comm.m Plot the histogram with the community size given a membership vector.
  • cycle_graph.m Generate the adjacency matrix of a cycle graph with n nodes.
  • effcommplot.m TODO
  • generate_agreement.m Generate the agreement matrix for a given community detection method.
  • generate_agreement_weighted.m Generate the weighted agreement matrix for a given community detection method.
  • generate_connected_components.m TODO
  • graph_JS_similarity.m Compute the quantum Jensen Shannon divergence between two adjacency matrices.
  • graph_laplacian.m Compute the graph combinatorial Laplacian matrix L=D-A.
  • group2membership.m Convert a cell of arrays representing the nodes in the communities to a membership vector.
  • image_to_network.m Convert a gray index image to its corresponding adjacency graph.
  • imagesctxt.m Show a matrix like imagesc but with text values of elements displayed on the pixels.
  • isoctave.m Returns true if using Octave, false if using Matlab.
  • jensen_shannon_sim.m Returns the Jensen-Shannon symmetrized information theoretic distance between two graph Laplacians.
  • k_regular.m Generate a k-regular graph, a graph where the degree of every vertex is k.
  • KL.m Returns the binary Kullback-Leibler divergence between Bernoulli distribution p and q.
  • kullback_leibler_sim.m Returns the Kullback-Leibler divergence between two graph Laplacians.
  • logHyperProbability.m Compute the logarithm of the hypergeometric probability in base 10.
  • membership2groups.m Convert a membership vector to a cell of arrays of nodes in every community.
  • membership_agreement.m DEPRECATE
  • membership_similarity.m TODO
  • method_best.m Functio handle to the community detection method that returns the best value over a set of repetitions.
  • modularity.m Returns the modularity of a graph with respect to a membership vector.
  • nearcorr.m Returns the nearest correlation matrix of a square matrix. Implementation by Nick Higham.
  • nearestSPD.m Returns the positive definite matrix of a square matrix. Implementation by Nick Higham.
  • norm_conf_mat.m DEPRECATE
  • number_connected_components.m Returns the number of connected components of a graph.
  • number_of_edges.m Returns the number of edges of a binary or weighted graph.
  • paco.m Function handle to the MEX implementation of PACO.
  • partition_params.m Returns the partition parameters for use with compute_surprise.
  • quantum_density.m Returns the quantum density of a graph, Brauenstein et al. "Ann. of Combinatorics, 10, no 3 (2006), 291-317."
  • reindex_membership.m Transform a membership vector to have community indices sorted by community size from 1 to |C|
  • reorder_membership.m Linearize a membership vector to have continuous indices of communities from 1 to |C|
  • ring_of_cliques.m Returns a network ring of cliques, with given number of cliques and clique size and its membership.
  • ring_of_custom_cliques.m Returns a network ring of cliques, with given size of cliques specified as input and its membership.
  • rmtdecompose.m Returns the Random Matrix Theory decomposition of a correlation matrix.
  • robustness_configuration_interp_und.m
  • robustness_edge_weight_und.m
  • run_cluster_similarity.m
  • rwalkent.m Returns the random walk entropy of a graph as in Estrada et al. Walk entropies in graphs, "Linear Algebra and its Applications 443 (2014) 235–244"
  • significance.m Returns the significance of a graph partitioning, Traag (2013).
  • smi.m Returns the standardized mutual information of two membership vectors.
  • sort_group_by_size.m Sort community groups by size.
  • star_of_custom_cliques.m Returns a star of cliques, every clique is connected to all other cliques with one edge.
  • surprise.m Returns the surprise of a graph partitioning.
  • threshold_by_giant_component.m Returns the threshold over which the graph has more than one connected component.
  • threshold_by_num_edges.m Returns a graph thresholded to have a specific number of edges.
  • vonneumann_entropy.m Returns the VonNeumann quantum entropy of graph Brauenstein et al. "Ann. of Combinatorics, 10, no 3 (2006), 291-317."
  • write_brainet.m Write a graph with coordinates of nodes and membership to Brainet format.
  • write_brainet_community.m TODO
  • writetoEdgesList.m TODO
  • writetoPAJ_labels.m
  • writetoPAJ_labels_coords.m

Python algorithms

  • find_intersections.py Find the intersections of edges in a bipartite graph (TODO)

Datasets

This repository contains the 638 areas template used by Crossley (2013). It consists of two files:

  1. template.nii is the NIFTI file describing the template, MNI space
  2. template_638.txt is the description of anatomical a

Additionally the file template_638_coords_abbr.txt is organized as follows

Node X Y Z Label Abbr

and differently from template_638.txt the NodeID starts from 0 to 637 (for indexing with Python).