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resilience.m
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resilience.m
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function [greedy_scores,ce,c,scores,T] = resilience(Adj,adj_weighted, nodes,rm_nodes,type )
% Written by: Udit Bhatia and Lina Sela
% Modified by: Udit Bhatia
% Date last modified: November 13, 2017
% Run as
%[greedy_scores,ce,c ] = resilience(adj_IRN,adj_IRN_weighted, nodes_irn,tsunami,"LargeC")
G = graph(Adj);
G.Nodes.id = nodes.id;
nnodes = size(G.Nodes,1);
nnlinks = size (G.Nodes,1);
var1 = rm_nodes.index;
list.nodes_indx = table2array(G.Edges);
list.nodes_indx(:,end) = [];
var2 = ismember(list.nodes_indx,var1);
var3 = +(sum(var2,2)>0);
rlist.edge_indx = find(var3);
rlist.nodes_indx = table2array(G.Edges(rlist.edge_indx,:));
rlist.nodes_indx(:,end) = [];
budget = length(rlist.edge_indx); % Number of edges removed
OD = adj_weighted;
% Requires greedy_lazy in working directory
% Originally coded by Lina Sela, UT Austin
[greedy.sset,greedy.scores,greedy.evalNum] = greedy_lazy(G, OD, rlist, budget,type);
Gr = graph(Adj);
scores = zeros(length(rlist.edge_indx),1);
for i = 1:length(rlist.edge_indx)
Gr = rmedge(Gr,rlist.nodes_indx(i,1),rlist.nodes_indx(i,2));
scores(i) = ODScore(Gr,OD,type);
end
if length(greedy.scores) < length(rlist.edge_indx)
temp1 = 1:length(rlist.edge_indx);
temp2 = setdiff(temp1,greedy.sset);
greedy.scores = [greedy.scores greedy.scores(end)*ones(1,length(temp2))];
greedy.sset = [greedy.sset temp2];
end
greedy.sset = rlist.edge_indx(greedy.sset);
Scores = [scores; greedy.scores'];
greedy_scores = Scores;
c.ucc = centrality(G,'closeness');
c.ud = centrality(G,'degree');
c.ubc = centrality(G,'betweenness');
c.ubc = 2*c.ubc/((nnodes-2)*(nnodes-1));
c.ueig = centrality(G,'eigenvector');
rnodes_indx = rm_nodes.index; % removed nodes index
% closenesspip in
[c.scores.ucc,c.reclist.ucc] = recover_centrality(G,OD,c.ucc,type,rnodes_indx,list);
[c.scores.ud,c.reclist.ud] = recover_centrality(G,OD,c.ud,type,rnodes_indx,list);
[c.scores.ubc,c.reclist.ubc] = recover_centrality(G,OD,c.ubc,type,rnodes_indx,list);
[c.scores.ueig,c.reclist.ueig] = recover_centrality(G,OD,c.ueig,type,rnodes_indx,list);
[ce] = Cross_entropy(budget,G, OD, type, rlist);
f = figure();
plot(Scores/max(Scores),'b','linewidth',3) % greedy
hold on
Scores2 = [scores; c.scores.ucc];% combine failure and recovery scores];
plot(Scores2/max(Scores2),'m','linewidth',3)
Scores2 = [scores; c.scores.ud]; % combine failure and recovery scores
plot(Scores2/max(Scores2),'k','linewidth',3)
Scores2 = [scores; c.scores.ubc]; % combine failure and recovery scores
plot(Scores2/max(Scores2),'g','linewidth',3)
Scores2 = [scores; c.scores.ueig]; % combine failure and recovery scores
plot(Scores2/max(Scores2),'y','linewidth',3)
Scores2 = [scores; ce.scores]; % combine failure and recovery scores
plot(Scores2/max(Scores2),'r','linewidth',3)
plot(scores/max(Scores2),'color',[0.5 0.5 0.5],'linewidth',3)
% plot([find(Scores == min(Scores)) find(Scores == min(Scores))],[0 max(Scores)],'--','color',[0.5 0.5 0.5])
plot([length(rlist.edge_indx) length(rlist.edge_indx)],[0 max(Scores)/max(Scores)],'--','color',[0.5 0.5 0.5])
xlim([0 length(Scores)])
ylim([0 max(Scores)/max(Scores)])
xlabel('Edge')
if strcmp(type,'OD')
ylabel('OD flow')
elseif strcmp(type,'LargeC')
ylabel('Largest component')
end
set(gca,'fontsize',16) % change font size
legend({'greedy','closeness','degree','betweenness','eigenvector','cross entropy'},'location','southeast')
legend('boxoff')
set(gca,'fontsize',16) % change font size
% grid on
h = gcf;
set(h,'PaperPositionMode','auto');
set(h,'PaperOrientation','landscape');
print(f,'-dpdf','fig_mbta_scores.pdf','-bestfit') % save as pdf file
nrlinks = length(rlist.edge_indx);
temp = greedy.scores;
greedy.R = nrlinks + 1 - ((temp(1) + temp(end))/2 + sum(temp(2:end-1)))/max(Scores);
temp = c.scores.ucc;
c.R.ucc = nrlinks + 1 - ((temp(1) + temp(end))/2 + sum(temp(2:end-1)))/max(Scores);
temp = c.scores.ud;
c.R.ud = nrlinks + 1 - ((temp(1) + temp(end))/2 + sum(temp(2:end-1)))/max(Scores);
temp = c.scores.ubc;
c.R.ubc = nrlinks + 1 - ((temp(1) + temp(end))/2 + sum(temp(2:end-1)))/max(Scores);
temp = c.scores.ueig;
c.R.ueig = nrlinks + 1 - ((temp(1) + temp(end))/2 + sum(temp(2:end-1)))/max(Scores);
temp = ce.scores;
ce.R = nrlinks + 1 - ((temp(1) + temp(end))/2 + sum(temp(2:end-1)))/max(Scores);
temp = round([greedy.R; c.R.ucc; c.R.ud; c.R.ubc; c.R.ueig; ce.R],2);
T = table(temp,'RowNames',{'greedy','closeness','degree','betweenness','eigenvector','cross entropy'});
end