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india_v1.m
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india_v1.m
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clc
clear
close all hidden
% build mbta network
% load data
%------------------------------------------------
load('Indian_datafiles.mat')
% load('mbta_data1.mat') % with winter 2015 event
% includes adjacency matrix, list of nodes, and list of nodes impacted by
% snow storm
Adj = adj_IRN;
nodes = nodes_irn;
G = graph(adj_IRN);
G.Nodes.id = nodes_irn.id;
nnodes = size(G.Nodes,1);
nlinks = size(G.Edges,1);
f = figure;
%worldmap('india')
p = plot(G,'layout','force');
p.Marker = 'o';
p.NodeColor = [0 0 0];
% p.NodeFaceColor = [1 1 1];
p.EdgeColor = [0 0 0];
temp = 5*ones(nnodes,1);
temp(tsunami.index) = 8;
G.Nodes.tsunami = temp;
p.MarkerSize = temp;
temp = zeros(nnodes,3);
for i=1:length(tsunami.index)
temp(tsunami.index(i),:) = [1 0 0];
end
p.NodeColor = temp;
p.YData = nodes_irn.lat;
p.XData = nodes_irn.lon;
% labelnode(p,nodes_snow.index,nodes_snow.id)
set(gca,'fontsize',16) % change font size
% grid on
f = gcf;
set(f,'PaperPositionMode','auto');
set(f,'PaperOrientation','landscape');
set(gca, 'XTick', [],'YTick', [],'Box', 'off','XColor','none','YColor','none','Color','none');
print(f,'-dpdf','fig_india_layout.pdf','-bestfit') % save as pdf file
print(f,'-dpng','fig_india_layout.png') % save as pdf file
var1 = tsunami.index; % nodes based on input data
list.nodes_indx = table2array(G.Edges); % get edge start/end nodes
list.nodes_indx(:,end) = []; % remove weights
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);
%------------------------------------------------
% made up OD matrix
%------------------------------------------------
OD = adj_IRN_weighted;
% list of edges that are removed from tsunami
%------------------------------------------------
% rlist.edge_indx = [1:3 30:35]; % index of edges that are removed from the full graph
var1 = tsunami.index; % nodes based on input data
list.nodes_indx = table2array(G.Edges); % get edge start/end nodes
list.nodes_indx(:,end) = []; % remove weights
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); % available budget
%------------------------------------------------
% pick the type of objective function to optimize
type = 'OD'; % od matrix
%type = 'LargeC'; % largest component
%------------------------------------------------
% greedy recovery
%------------------------------------------------
% G - original graph; OD - matrix; rlist - list of removed edgest; budget;
% type of functionality
[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']; % combine failure and recovery scores
%%
draw = 'true';
if strcmp(draw,'true')
f = figure();
plot(Scores/max(Scores))
hold on
% 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([0.8 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
end
c.ucc = centrality(G,'closeness');
c.ud = centrality(G,'degree');
c.ubc = centrality(G,'betweenness');
c.ubc = 2*c.ubc/((nnodes-2)*(nnodes-1));
maxim = find(c.ubc == max(c.ubc(:)));
c.ueig = centrality(G,'eigenvector');
draw = 'false';
if strcmp(draw,'true')
% plot distribution of centrality measures
%------------------------------------------------
f = figure;
p = plot(G,'layout','force');
p.XData = nodes.lon;
p.YData = nodes.lat;
p.NodeCData = c.ucc;
colormap jet
colorbar
title('Closeness Centrality Scores - Unweighted')
f = figure;
p = plot(G,'layout','force');
p.XData = nodes.lon;
p.YData = nodes.lat;
p.NodeCData = c.ubc;
colormap jet
colorbar
title('Betweenness Centrality Scores - Unweighted')
end
% recovery: greedy, ucc, ud, ubc, ueig
%------------------------------------------------
rnodes_indx = tsunami.index; % removed nodes index
% closeness
[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_v1