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authorKatolaZ <katolaz@freaknet.org>2017-09-27 15:06:31 +0100
committerKatolaZ <katolaz@freaknet.org>2017-09-27 15:06:31 +0100
commit3aee2fd43e3059a699af2b63c6f2395e5a55e515 (patch)
tree58c95505a0906ed9cfa694f9dbd319403fd8f01d /src/bb_fitness/bb_fitness.c
First commit on github -- NetBunch 1.0
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diff --git a/src/bb_fitness/bb_fitness.c b/src/bb_fitness/bb_fitness.c
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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);
+
+}