37 lines
1.1 KiB
Matlab
37 lines
1.1 KiB
Matlab
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% Compute the clustering coefficient per node.
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% Ci = the average local clustering, where Ci = (number of triangles connected to i) / (number of triples centered on i)
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% Ref: M. E. J. Newman, "The structure and function of complex networks"
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% Note: Valid for directed and undirected graphs
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%
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% INPUT: adjacency matrix, nxn
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% OUTPUT: the average clustering coefficient (aveC) and the
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% clustering coefficient vector C per node (where mean(C) = aveC)
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%
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% Other routines used: degrees.m, isDirected.m, kneighbors.m,
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% numEdges.m, subgraph.m
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% GB, Last updated: February 7, 2015
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function [aveC,C] = clustCoeff(adj)
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n = length(adj);
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adj = adj>0; % no multiple edges
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[deg,~,~] = degrees(adj);
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C=zeros(n,1); % initialize clustering coefficient
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% multiplication change in the clust coeff formula
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% to reflect the edge count for directed/undirected graphs
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coeff = 2;
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if isDirected(adj); coeff=1; end
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for i=1:n
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if deg(i)==1 || deg(i)==0; C(i)=0; continue; end
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neigh=kneighbors(adj,i,1);
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edges_s=numEdges(subgraph(adj,neigh));
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C(i)=coeff*edges_s/(deg(i)*(deg(i)-1));
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end
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aveC=sum(C)/n;
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