Complete the code below with an example graph:

#include

#include

#include

$//$ Define your graph data structure here

$//$ For simplicity, let's assume an adjacency matrix representation

#define MAX$\_$VERTICES $100$

int graph$[$MAX$\_$VERTICES$][$MAX$\_$VERTICES$]$;

$//$ Function to compute betweenness centrality for a subset of vertices

void computeBetweenness$($int $*$graph$,$ int num$\_$vertices, int start$\_$vertex, int end$\_$vertex$)\{$

$//$ Implementation of Brandes' algorithm here

$//$ Placeholder implementation

printf$("$Computing betweenness centrality for vertices $\%$d to $\%$d

$",$ start$\_$vertex, end$\_$vertex$)$;

$\}$

int main$($int argc, char $**$argv$)\{$

MPI$\_$Init$($&argc, &argv$)$;

int rank, size;

MPI$\_$Comm$\_$rank$($MPI$\_$COMM$\_$WORLD, &rank$)$;

MPI$\_$Comm$\_$size$($MPI$\_$COMM$\_$WORLD, &size$)$;

$//$ Assume graph data is distributed among processes

$//$ Define data distribution strategy

int num$\_$vertices $=10$; $//$ Number of vertices in the graph

int start$\_$vertex $=$ rank $*($num$\_$vertices $/$ size$)$;

int end$\_$vertex $=($rank $+1)*($num$\_$vertices $/$ size$)-1$;

$//$ Scatter graph data among processes

int local$\_$graph$[$num$\_$vertices$][$num$\_$vertices $/$ size$]$;

MPI$\_$Scatter$($graph$,$ num$\_$vertices $*$ num$\_$vertices $/$ size, MPI$\_$INT, local$\_$graph, num$\_$vertices $*$ num$\_$vertices $/$ size, MPI$\_$INT, $0,$ MPI$\_$COMM$\_$WORLD$)$;

$//$ Compute betweenness centrality for vertices assigned to this process

computeBetweenness$($local$\_$graph, num$\_$vertices, start$\_$vertex, end$\_$vertex$)$;

$//$ Gather results from all processes

$//$ MPI$\_$Gather$(...)$;

MPI$\_$Finalize$()$;

return $0$;

$\}$