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/**
* This program is free software: you can redistribute it and/or
* modify it under the terms of the GNU General Public License as
* published by the Free Software Foundation, either version 3 of the
* License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see
* <http://www.gnu.org/licenses/>.
*
* (c) Vincenzo Nicosia 2009-2017 -- <v.nicosia@qmul.ac.uk>
*
* This file is part of NetBunch, a package for complex network
* analysis and modelling. For more information please visit:
*
* http://www.complex-networks.net/
*
* If you use this software, please add a reference to
*
* V. Latora, V. Nicosia, G. Russo
* "Complex Networks: Principles, Methods and Applications"
* Cambridge University Press (2017)
* ISBN: 9781107103184
*
***********************************************************************
*
* This program implements the fitness model proposed by Bianconi and
* Barabasi, where the attachment probability is:
*
* \Pi_{i->j} \propto a_j * k_j
*
* where a_j is the actractiveness of node j.
*
*
* References:
*
* [1] G. Bianconi, A.-L. Barabasi, " Competition and multiscaling in
* evolving networks". EPL-Europhys. Lett. 54 (2001), 436.
*
*/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include "utils.h"
#include "cum_distr.h"
void usage(char *argv[]){
printf("********************************************************************\n"
"** **\n"
"** -*- bb_fitness -*- **\n"
"** **\n"
"** Grow a network of 'N' nodes using the fitness model proposed **\n"
"** by Bianconi and Barabasi. **\n"
"** **\n"
"** The initial network is a clique of 'n0' nodes, and each new **\n"
"** node creates 'm' edges. The attachment probability is of **\n"
"** the form: **\n"
"** **\n"
"** P(i->j) ~ a_j * k_j **\n"
"** **\n"
"** where a_j is the attractiveness of node j. The values of **\n"
"** node attractiveness are sampled uniformly at random in **\n"
"** [0,1]. **\n"
"** **\n"
"** The program prints on STDOUT the edge-list of the final **\n"
"** graph. **\n"
"** **\n"
"** If 'FIT' is specified as a fourth parameter, the values **\n"
"** of node attractiveness are printed on STDERR. **\n"
"** **\n"
"********************************************************************\n"
" This is Free Software - You can use and distribute it under \n"
" the terms of the GNU General Public License, version 3 or later\n\n"
" (c) Vincenzo Nicosia 2009-2017 (v.nicosia@qmul.ac.uk)\n\n"
"********************************************************************\n\n"
);
printf("Usage: %s <N> <m> <n0> [SHOW]\n", argv[0]);
}
int init_network(unsigned int *I, unsigned int *J, int n0,
double *a, cum_distr_t *d){
unsigned int n, i, S_num;
S_num = 0;
for(n=0; n<n0; n++){
for(i=n+1; i<n0; i++){
I[S_num] = n;
J[S_num] = i % n0;
S_num += 1;
}
cum_distr_add(d, n, n0*a[n]);
}
return S_num;
}
int already_neighbour(unsigned int *J, int S_num, int j, int dest){
int i;
for(i=S_num; i< S_num + j; i ++){
if (J[i] == dest)
return 1;
}
return 0;
}
int bb_fitness(unsigned int *I, unsigned int *J, unsigned int N,
unsigned int m, unsigned int n0, double* a){
cum_distr_t *d = NULL;
unsigned int n, j, dest, S_num;
d = cum_distr_init(N * m);
S_num = init_network(I, J, n0, a, d);
n = n0;
while (n<N){
for(j=0; j<m; j++){
I[S_num+j] = n;
dest = cum_distr_sample(d);
while(already_neighbour(J, S_num, j, dest)){
dest = cum_distr_sample(d);
}
J[S_num + j] = dest;
}
cum_distr_add(d, n, m*a[n]);
for (j=0; j<m; j++){
cum_distr_add(d, J[S_num + j], a[ J[S_num + j] ]);
}
S_num += m;
n += 1;
}
cum_distr_destroy(d);
return S_num;
}
void dump_graph(unsigned int *I, unsigned int *J, unsigned int K){
unsigned int i;
for(i=0; i<K; i++){
printf("%d %d\n", J[i], I[i]);
}
}
void init_fitness_uniform(double *a, unsigned int N){
unsigned int i;
for(i=0; i<N; i++){
a[i] = 1.0 * rand() / RAND_MAX;
}
}
void dump_fitness(double *a, unsigned int N){
int i;
for(i=0; i<N; i++){
fprintf(stderr, "%g\n", a[i]);
}
}
int main(int argc, char *argv[]){
int N, m, n0, K;
unsigned int *I, *J;
double *a;
if (argc < 4){
usage(argv);
exit(1);
}
N = atoi(argv[1]);
m = atoi(argv[2]);
n0 = atoi(argv[3]);
a = malloc(N * sizeof(double));
srand(time(NULL));
if (N < 1){
fprintf(stderr, "N must be positive\n");
exit(1);
}
if(m > n0){
fprintf(stderr, "n0 cannot be smaller than m\n");
exit(1);
}
if (n0<1){
fprintf(stderr, "n0 must be positive\n");
exit(1);
}
if (m < 1){
fprintf(stderr, "m must be positive\n");
exit(1);
}
I = malloc(N * m * sizeof(unsigned int));
J = malloc(N * m * sizeof(unsigned int));
a = malloc(N * sizeof(double));
init_fitness_uniform(a, N);
K = bb_fitness(I, J, N, m, n0, a);
dump_graph(I, J, K);
if (argc > 4 && !my_strcasecmp(argv[4], "SHOW")){
dump_fitness(a, N);
}
free(a);
free(I);
free(J);
}
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