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Diffstat (limited to 'src/bb_fitness/bb_fitness.c')
| -rw-r--r-- | src/bb_fitness/bb_fitness.c | 241 |
1 files changed, 241 insertions, 0 deletions
diff --git a/src/bb_fitness/bb_fitness.c b/src/bb_fitness/bb_fitness.c new file mode 100644 index 0000000..9f67c74 --- /dev/null +++ b/src/bb_fitness/bb_fitness.c @@ -0,0 +1,241 @@ +/** + * 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); + +} |