communityalg
Algorithms and functions in Matlab for community detection in networks.
Expands BrainConnectivity toolbox.
Matlab/Octave algorithms and functions
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ami.m Returns the adjusted mutual information between two membership vectors.
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association_score.m Returns the association score between pairs of communities specified by the graph and membership.
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asymptotic_modularity.m Compute asymptotic modularity of a graph with respect to a membership vector.
-
asymptotic_modularity_sum.m TODO
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asymptotic_surprise.m Compute asymptotic surprise of a graph with respect to a membership vector.
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clique.m Generate an adjacency matrix of a clique graph with
n
nodes.
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cluster_similarity.m Compare two membership vectors.
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clustering_entropy.m Compute the clustering entropy of an agreement matrix as in "Gfeller, Newman, 2006".
-
community_robustness_weighted.m TODO
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community_size2memb.m Convert an array where every element is the size of a clique to the correspondig membership vector.
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comm_mat.m Returns the block matrix of a graph and its community structure as membership.
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compute_surprise.m Compute the surprise given surprise paramenters.
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consensus_clustering.m TODO
-
consensus_clustering_weighted.m TODO
-
consensus_entropy.m TODO
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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
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generate_agreement.m Generate the agreement matrix for a given community detection method.
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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
.
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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.
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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.
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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.
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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) 235244"
-
significance.m Returns the significance of a graph partitioning,
Traag (2013)
.
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smi.m Returns the standardized mutual information of two membership vectors.
-
sort_group_by_size.m Sort community groups by size.
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star_of_custom_cliques.m Returns a star of cliques, every clique is connected to all other cliques with one edge.
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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.
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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:
-
template.nii
is the NIFTI file describing the template, MNI space
-
template_638.txt
is the description of anatomical a
Additionally the file template_638_coords_abbr.txt
is organized as follows
and differently from template_638.txt
the NodeID starts from 0 to 637 (for indexing with Python).