/**
* \ingroup structural
* \function igraph_similarity_jaccard
* \brief Jaccard similarity coefficient for the given vertices.
*
* </para><para>
* The Jaccard similarity coefficient of two vertices is the number of common
* neighbors divided by the number of vertices that are neighbors of at
* least one of the two vertices being considered. This function calculates
* the pairwise Jaccard similarities for some (or all) of the vertices.
*
* \param graph The graph object to analyze
* \param res Pointer to a matrix, the result of the calculation will
* be stored here. The number of its rows and columns is the same
* as the number of vertex ids in \p vids.
* \param vids The vertex ids of the vertices for which the
* calculation will be done.
* \param mode The type of neighbors to be used for the calculation in
* directed graphs. Possible values:
* \clist
* \cli IGRAPH_OUT
* the outgoing edges will be considered for each node.
* \cli IGRAPH_IN
* the incoming edges will be considered for each node.
* \cli IGRAPH_ALL
* the directed graph is considered as an undirected one for the
* computation.
* \endclist
* \param loops Whether to include the vertices themselves in the neighbor
* sets.
* \return Error code:
* \clist
* \cli IGRAPH_ENOMEM
* not enough memory for temporary data.
* \cli IGRAPH_EINVVID
* invalid vertex id passed.
* \cli IGRAPH_EINVMODE
* invalid mode argument.
* \endclist
*
* Time complexity: O(|V|^2 d),
* |V| is the number of vertices in the vertex iterator given, d is the
* (maximum) degree of the vertices in the graph.
*
* \sa \ref igraph_similarity_dice(), a measure very similar to the Jaccard
* coefficient
*
* \example examples/simple/igraph_similarity.c
*/
int igraph_similarity_jaccard(const igraph_t *graph, igraph_matrix_t *res,
const igraph_vs_t vids, igraph_neimode_t mode, igraph_bool_t loops) {
igraph_lazy_adjlist_t al;
igraph_vit_t vit, vit2;
long int i, j, k;
long int len_union, len_intersection;
igraph_vector_t *v1, *v2;
IGRAPH_CHECK(igraph_vit_create(graph, vids, &vit));
IGRAPH_FINALLY(igraph_vit_destroy, &vit);
IGRAPH_CHECK(igraph_vit_create(graph, vids, &vit2));
IGRAPH_FINALLY(igraph_vit_destroy, &vit2);
IGRAPH_CHECK(igraph_lazy_adjlist_init(graph, &al, mode, IGRAPH_SIMPLIFY));
IGRAPH_FINALLY(igraph_lazy_adjlist_destroy, &al);
IGRAPH_CHECK(igraph_matrix_resize(res, IGRAPH_VIT_SIZE(vit), IGRAPH_VIT_SIZE(vit)));
if (loops) {
for (IGRAPH_VIT_RESET(vit); !IGRAPH_VIT_END(vit); IGRAPH_VIT_NEXT(vit)) {
i=IGRAPH_VIT_GET(vit);
v1=igraph_lazy_adjlist_get(&al, (igraph_integer_t) i);
if (!igraph_vector_binsearch(v1, i, &k))
igraph_vector_insert(v1, k, i);
}
}
for (IGRAPH_VIT_RESET(vit), i=0;
!IGRAPH_VIT_END(vit); IGRAPH_VIT_NEXT(vit), i++) {
MATRIX(*res, i, i) = 1.0;
for (IGRAPH_VIT_RESET(vit2), j=0;
!IGRAPH_VIT_END(vit2); IGRAPH_VIT_NEXT(vit2), j++) {
if (j <= i)
continue;
v1=igraph_lazy_adjlist_get(&al, IGRAPH_VIT_GET(vit));
v2=igraph_lazy_adjlist_get(&al, IGRAPH_VIT_GET(vit2));
igraph_i_neisets_intersect(v1, v2, &len_union, &len_intersection);
if (len_union > 0)
MATRIX(*res, i, j) = ((igraph_real_t)len_intersection)/len_union;
else
MATRIX(*res, i, j) = 0.0;
MATRIX(*res, j, i) = MATRIX(*res, i, j);
}
}
igraph_lazy_adjlist_destroy(&al);
igraph_vit_destroy(&vit);
igraph_vit_destroy(&vit2);
IGRAPH_FINALLY_CLEAN(3);
return 0;
}
int igraph_i_maximal_cliques(const igraph_t *graph, igraph_i_maximal_clique_func_t func, void* data) {
int directed=igraph_is_directed(graph);
long int i, j, k, l;
igraph_integer_t no_of_nodes, nodes_to_check, nodes_done;
igraph_integer_t best_cand = 0, best_cand_degree = 0, best_fini_cand_degree;
igraph_adjlist_t adj_list;
igraph_stack_ptr_t stack;
igraph_i_maximal_cliques_stack_frame frame, *new_frame_ptr;
igraph_vector_t clique, new_cand, new_fini, cn, best_cand_nbrs, best_fini_cand_nbrs;
igraph_bool_t cont = 1;
int assret;
if (directed)
IGRAPH_WARNING("directionality of edges is ignored for directed graphs");
no_of_nodes = igraph_vcount(graph);
if (no_of_nodes == 0)
return IGRAPH_SUCCESS;
/* Construct an adjacency list representation */
IGRAPH_CHECK(igraph_adjlist_init(graph, &adj_list, IGRAPH_ALL));
IGRAPH_FINALLY(igraph_adjlist_destroy, &adj_list);
IGRAPH_CHECK(igraph_adjlist_simplify(&adj_list));
igraph_adjlist_sort(&adj_list);
/* Initialize stack */
IGRAPH_CHECK(igraph_stack_ptr_init(&stack, 0));
IGRAPH_FINALLY(igraph_i_maximal_cliques_stack_destroy, &stack);
/* Create the initial (empty) clique */
IGRAPH_VECTOR_INIT_FINALLY(&clique, 0);
/* Initialize new_cand, new_fini, cn, best_cand_nbrs and best_fini_cand_nbrs (will be used later) */
IGRAPH_VECTOR_INIT_FINALLY(&new_cand, 0);
IGRAPH_VECTOR_INIT_FINALLY(&new_fini, 0);
IGRAPH_VECTOR_INIT_FINALLY(&cn, 0);
IGRAPH_VECTOR_INIT_FINALLY(&best_cand_nbrs, 0);
IGRAPH_VECTOR_INIT_FINALLY(&best_fini_cand_nbrs, 0);
/* Find the vertex with the highest degree */
best_cand = 0; best_cand_degree = igraph_vector_size(igraph_adjlist_get(&adj_list, 0));
for (i = 1; i < no_of_nodes; i++) {
j = igraph_vector_size(igraph_adjlist_get(&adj_list, i));
if (j > best_cand_degree) {
best_cand = i;
best_cand_degree = j;
}
}
/* Create the initial stack frame */
IGRAPH_CHECK(igraph_vector_init_seq(&frame.cand, 0, no_of_nodes-1));
IGRAPH_FINALLY(igraph_vector_destroy, &frame.cand);
IGRAPH_CHECK(igraph_vector_init(&frame.fini, 0));
IGRAPH_FINALLY(igraph_vector_destroy, &frame.fini);
IGRAPH_CHECK(igraph_vector_init(&frame.cand_filtered, 0));
IGRAPH_FINALLY(igraph_vector_destroy, &frame.cand_filtered);
IGRAPH_CHECK(igraph_vector_difference_sorted(&frame.cand,
igraph_adjlist_get(&adj_list, best_cand), &frame.cand_filtered));
IGRAPH_FINALLY_CLEAN(3);
IGRAPH_FINALLY(igraph_i_maximal_cliques_stack_frame_destroy, &frame);
/* TODO: frame.cand and frame.fini should be a set instead of a vector */
/* Main loop starts here */
nodes_to_check = igraph_vector_size(&frame.cand_filtered); nodes_done = 0;
while (!igraph_vector_empty(&frame.cand_filtered) || !igraph_stack_ptr_empty(&stack)) {
if (igraph_vector_empty(&frame.cand_filtered)) {
/* No candidates left to check in this stack frame, pop out the previous stack frame */
igraph_i_maximal_cliques_stack_frame *newframe = igraph_stack_ptr_pop(&stack);
igraph_i_maximal_cliques_stack_frame_destroy(&frame);
frame = *newframe;
free(newframe);
if (igraph_stack_ptr_size(&stack) == 1) {
/* We will be using the next candidate node in the next iteration, so we can increase
* nodes_done by 1 */
nodes_done++;
}
/* For efficiency reasons, we only check for interruption and show progress here */
IGRAPH_PROGRESS("Maximal cliques: ", 100.0 * nodes_done / nodes_to_check, NULL);
IGRAPH_ALLOW_INTERRUPTION();
igraph_vector_pop_back(&clique);
continue;
}
/* Try the next node in the clique */
i = igraph_vector_pop_back(&frame.cand_filtered);
IGRAPH_CHECK(igraph_vector_push_back(&clique, i));
/* Remove the node from the candidate list */
assret=igraph_vector_binsearch(&frame.cand, i, &j); assert(assret);
igraph_vector_remove(&frame.cand, j);
/* Add the node to the finished list */
assret = !igraph_vector_binsearch(&frame.fini, i, &j); assert(assret);
IGRAPH_CHECK(igraph_vector_insert(&frame.fini, j, i));
/* Create new_cand and new_fini */
IGRAPH_CHECK(igraph_vector_intersect_sorted(&frame.cand, igraph_adjlist_get(&adj_list, i), &new_cand));
IGRAPH_CHECK(igraph_vector_intersect_sorted(&frame.fini, igraph_adjlist_get(&adj_list, i), &new_fini));
/* Do we have anything more to search? */
if (igraph_vector_empty(&new_cand)) {
if (igraph_vector_empty(&new_fini)) {
/* We have a maximal clique here */
IGRAPH_CHECK(func(&clique, data, &cont));
if (!cont) {
/* The callback function requested to stop the search */
break;
}
}
igraph_vector_pop_back(&clique);
continue;
}
if (igraph_vector_empty(&new_fini) && igraph_vector_size(&new_cand) == 1) {
/* Shortcut: only one node left */
IGRAPH_CHECK(igraph_vector_push_back(&clique, VECTOR(new_cand)[0]));
IGRAPH_CHECK(func(&clique, data, &cont));
if (!cont) {
/* The callback function requested to stop the search */
break;
}
igraph_vector_pop_back(&clique);
igraph_vector_pop_back(&clique);
continue;
}
/* Find the next best candidate node in new_fini */
l = igraph_vector_size(&new_cand);
best_cand_degree = -1;
j = igraph_vector_size(&new_fini);
for (i = 0; i < j; i++) {
k = (long int)VECTOR(new_fini)[i];
IGRAPH_CHECK(igraph_vector_intersect_sorted(&new_cand, igraph_adjlist_get(&adj_list, k), &cn));
if (igraph_vector_size(&cn) > best_cand_degree) {
best_cand_degree = igraph_vector_size(&cn);
IGRAPH_CHECK(igraph_vector_update(&best_fini_cand_nbrs, &cn));
if (best_cand_degree == l) {
/* Cool, we surely have the best candidate node here as best_cand_degree can't get any better */
break;
}
}
}
/* Shortcut here: we don't have to examine new_cand */
if (best_cand_degree == l) {
igraph_vector_pop_back(&clique);
continue;
}
/* Still finding best candidate node */
best_fini_cand_degree = best_cand_degree;
best_cand_degree = -1;
j = igraph_vector_size(&new_cand);
l = l - 1;
for (i = 0; i < j; i++) {
k = (long int)VECTOR(new_cand)[i];
IGRAPH_CHECK(igraph_vector_intersect_sorted(&new_cand, igraph_adjlist_get(&adj_list, k), &cn));
if (igraph_vector_size(&cn) > best_cand_degree) {
best_cand_degree = igraph_vector_size(&cn);
IGRAPH_CHECK(igraph_vector_update(&best_cand_nbrs, &cn));
if (best_cand_degree == l) {
/* Cool, we surely have the best candidate node here as best_cand_degree can't get any better */
break;
}
}
}
/* Create a new stack frame in case we back out later */
new_frame_ptr = igraph_Calloc(1, igraph_i_maximal_cliques_stack_frame);
if (new_frame_ptr == 0) {
IGRAPH_ERROR("cannot allocate new stack frame", IGRAPH_ENOMEM);
}
IGRAPH_FINALLY(igraph_free, new_frame_ptr);
*new_frame_ptr = frame;
memset(&frame, 0, sizeof(frame));
IGRAPH_CHECK(igraph_stack_ptr_push(&stack, new_frame_ptr));
IGRAPH_FINALLY_CLEAN(1); /* ownership of new_frame_ptr taken by the stack */
/* Ownership of the current frame and its vectors (frame.cand, frame.done, frame.cand_filtered)
* is taken by the stack from now on. Vectors in frame must be re-initialized with new_cand,
* new_fini and stuff. The old frame.cand and frame.fini won't be leaked because they are
* managed by the stack now. */
frame.cand = new_cand;
frame.fini = new_fini;
IGRAPH_CHECK(igraph_vector_init(&new_cand, 0));
IGRAPH_CHECK(igraph_vector_init(&new_fini, 0));
IGRAPH_CHECK(igraph_vector_init(&frame.cand_filtered, 0));
/* Adjust frame.cand_filtered */
if (best_cand_degree < best_fini_cand_degree) {
IGRAPH_CHECK(igraph_vector_difference_sorted(&frame.cand, &best_fini_cand_nbrs, &frame.cand_filtered));
} else {
IGRAPH_CHECK(igraph_vector_difference_sorted(&frame.cand, &best_cand_nbrs, &frame.cand_filtered));
}
}
IGRAPH_PROGRESS("Maximal cliques: ", 100.0, NULL);
igraph_adjlist_destroy(&adj_list);
igraph_vector_destroy(&clique);
igraph_vector_destroy(&new_cand);
igraph_vector_destroy(&new_fini);
igraph_vector_destroy(&cn);
igraph_vector_destroy(&best_cand_nbrs);
igraph_vector_destroy(&best_fini_cand_nbrs);
igraph_i_maximal_cliques_stack_frame_destroy(&frame);
igraph_i_maximal_cliques_stack_destroy(&stack);
IGRAPH_FINALLY_CLEAN(9);
return IGRAPH_SUCCESS;
}
igraph_vector_t * ggen_analyze_longest_antichain(igraph_t *g)
{
/* The following steps are implemented :
* - Convert our DAG to a specific bipartite graph B
* - solve maximum matching on B
* - conver maximum matching to min vectex cover
* - convert min vertex cover to antichain on G
*/
int err;
unsigned long i,vg,found,added;
igraph_t b,gstar;
igraph_vector_t edges,*res = NULL;
igraph_vector_t c,s,t,todo,n,next,l,r;
igraph_eit_t eit;
igraph_es_t es;
igraph_integer_t from,to;
igraph_vit_t vit;
igraph_vs_t vs;
igraph_real_t value;
if(g == NULL)
return NULL;
/* before creating the bipartite graph, we need all relations
* between any two vertices : the transitive closure of g */
err = igraph_copy(&gstar,g);
if(err) return NULL;
err = ggen_transform_transitive_closure(&gstar);
if(err) goto error;
/* Bipartite convertion : let G = (S,C),
* we build B = (U,V,E) with
* - U = V = S (each vertex is present twice)
* - (u,v) \in E iff :
* - u \in U
* - v \in V
* - u < v in C (warning, this means that we take
* transitive closure into account, not just the
* original edges)
* We will also need two additional nodes further in the code.
*/
vg = igraph_vcount(g);
err = igraph_empty(&b,vg*2,1);
if(err) goto error;
/* id and id+vg will be a vertex in U and its copy in V,
* iterate over gstar edges to create edges in b
*/
err = igraph_vector_init(&edges,igraph_ecount(&gstar));
if(err) goto d_b;
igraph_vector_clear(&edges);
err = igraph_eit_create(&gstar,igraph_ess_all(IGRAPH_EDGEORDER_ID),&eit);
if(err) goto d_edges;
for(IGRAPH_EIT_RESET(eit); !IGRAPH_EIT_END(eit); IGRAPH_EIT_NEXT(eit))
{
err = igraph_edge(&gstar,IGRAPH_EIT_GET(eit),&from,&to);
if(err)
{
igraph_eit_destroy(&eit);
goto d_edges;
}
to += vg;
igraph_vector_push_back(&edges,(igraph_real_t)from);
igraph_vector_push_back(&edges,(igraph_real_t)to);
}
igraph_eit_destroy(&eit);
err = igraph_add_edges(&b,&edges,NULL);
if(err) goto d_edges;
/* maximum matching on b */
igraph_vector_clear(&edges);
err = bipartite_maximum_matching(&b,&edges);
if(err) goto d_edges;
/* Let M be the max matching, and N be E - M
* Define T as all unmatched vectices from U as well as all vertices
* reachable from those by going left-to-right along N and right-to-left along
* M.
* Define L = U - T, R = V \inter T
* C:= L + R
* C is a minimum vertex cover
*/
err = igraph_vector_init_seq(&n,0,igraph_ecount(&b)-1);
if(err) goto d_edges;
err = vector_diff(&n,&edges);
if(err) goto d_n;
err = igraph_vector_init(&c,vg);
if(err) goto d_n;
igraph_vector_clear(&c);
/* matched vertices : S */
err = igraph_vector_init(&s,vg);
if(err) goto d_c;
igraph_vector_clear(&s);
for(i = 0; i < igraph_vector_size(&edges); i++)
{
err = igraph_edge(&b,VECTOR(edges)[i],&from,&to);
if(err) goto d_s;
igraph_vector_push_back(&s,from);
}
/* we may have inserted the same vertex multiple times */
err = vector_uniq(&s);
if(err) goto d_s;
/* unmatched */
err = igraph_vector_init_seq(&t,0,vg-1);
if(err) goto d_s;
err = vector_diff(&t,&s);
if(err) goto d_t;
/* alternating paths
*/
err = igraph_vector_copy(&todo,&t);
if(err) goto d_t;
err = igraph_vector_init(&next,vg);
if(err) goto d_todo;
igraph_vector_clear(&next);
do {
vector_uniq(&todo);
added = 0;
for(i = 0; i < igraph_vector_size(&todo); i++)
{
if(VECTOR(todo)[i] < vg)
{
/* scan edges */
err = igraph_es_adj(&es,VECTOR(todo)[i],IGRAPH_OUT);
if(err) goto d_next;
err = igraph_eit_create(&b,es,&eit);
if(err)
{
igraph_es_destroy(&es);
goto d_next;
}
for(IGRAPH_EIT_RESET(eit); !IGRAPH_EIT_END(eit); IGRAPH_EIT_NEXT(eit))
{
if(igraph_vector_binsearch(&n,IGRAPH_EIT_GET(eit),NULL))
{
err = igraph_edge(&b,IGRAPH_EIT_GET(eit),&from,&to);
if(err)
{
igraph_eit_destroy(&eit);
igraph_es_destroy(&es);
goto d_next;
}
if(!igraph_vector_binsearch(&t,to,NULL))
{
igraph_vector_push_back(&next,to);
added = 1;
}
}
}
}
else
{
/* scan edges */
err = igraph_es_adj(&es,VECTOR(todo)[i],IGRAPH_IN);
if(err) goto d_next;
err = igraph_eit_create(&b,es,&eit);
if(err)
{
igraph_es_destroy(&es);
goto d_next;
}
for(IGRAPH_EIT_RESET(eit); !IGRAPH_EIT_END(eit); IGRAPH_EIT_NEXT(eit))
{
if(igraph_vector_binsearch(&edges,IGRAPH_EIT_GET(eit),NULL))
{
err = igraph_edge(&b,IGRAPH_EIT_GET(eit),&from,&to);
if(err)
{
igraph_eit_destroy(&eit);
igraph_es_destroy(&es);
goto d_next;
}
if(!igraph_vector_binsearch(&t,to,NULL))
{
igraph_vector_push_back(&next,from);
added = 1;
}
}
}
}
igraph_es_destroy(&es);
igraph_eit_destroy(&eit);
}
igraph_vector_append(&t,&todo);
igraph_vector_clear(&todo);
igraph_vector_append(&todo,&next);
igraph_vector_clear(&next);
} while(added);
err = igraph_vector_init_seq(&l,0,vg-1);
if(err) goto d_t;
err = vector_diff(&l,&t);
if(err) goto d_l;
err = igraph_vector_update(&c,&l);
if(err) goto d_l;
err = igraph_vector_init(&r,vg);
if(err) goto d_l;
igraph_vector_clear(&r);
/* compute V \inter T */
for(i = 0; i < igraph_vector_size(&t); i++)
{
if(VECTOR(t)[i] >= vg)
igraph_vector_push_back(&r,VECTOR(t)[i]);
}
igraph_vector_add_constant(&r,(igraph_real_t)-vg);
err = vector_union(&c,&r);
if(err) goto d_r;
/* our antichain is U - C */
res = malloc(sizeof(igraph_vector_t));
if(res == NULL) goto d_r;
err = igraph_vector_init_seq(res,0,vg-1);
if(err) goto f_res;
err = vector_diff(res,&c);
if(err) goto d_res;
goto ret;
d_res:
igraph_vector_destroy(res);
f_res:
free(res);
res = NULL;
ret:
d_r:
igraph_vector_destroy(&r);
d_l:
igraph_vector_destroy(&l);
d_next:
igraph_vector_destroy(&next);
d_todo:
igraph_vector_destroy(&todo);
d_t:
igraph_vector_destroy(&t);
d_s:
igraph_vector_destroy(&s);
d_c:
igraph_vector_destroy(&c);
d_n:
igraph_vector_destroy(&n);
d_edges:
igraph_vector_destroy(&edges);
d_b:
igraph_destroy(&b);
error:
igraph_destroy(&gstar);
return res;
}
/**
* \ingroup structural
* \function igraph_similarity_jaccard_pairs
* \brief Jaccard similarity coefficient for given vertex pairs.
*
* </para><para>
* The Jaccard similarity coefficient of two vertices is the number of common
* neighbors divided by the number of vertices that are neighbors of at
* least one of the two vertices being considered. This function calculates
* the pairwise Jaccard similarities for a list of vertex pairs.
*
* \param graph The graph object to analyze
* \param res Pointer to a vector, the result of the calculation will
* be stored here. The number of elements is the same as the number
* of pairs in \p pairs.
* \param pairs A vector that contains the pairs for which the similarity
* will be calculated. Each pair is defined by two consecutive elements,
* i.e. the first and second element of the vector specifies the first
* pair, the third and fourth element specifies the second pair and so on.
* \param mode The type of neighbors to be used for the calculation in
* directed graphs. Possible values:
* \clist
* \cli IGRAPH_OUT
* the outgoing edges will be considered for each node.
* \cli IGRAPH_IN
* the incoming edges will be considered for each node.
* \cli IGRAPH_ALL
* the directed graph is considered as an undirected one for the
* computation.
* \endclist
* \param loops Whether to include the vertices themselves in the neighbor
* sets.
* \return Error code:
* \clist
* \cli IGRAPH_ENOMEM
* not enough memory for temporary data.
* \cli IGRAPH_EINVVID
* invalid vertex id passed.
* \cli IGRAPH_EINVMODE
* invalid mode argument.
* \endclist
*
* Time complexity: O(nd), n is the number of pairs in the given vector, d is
* the (maximum) degree of the vertices in the graph.
*
* \sa \ref igraph_similarity_jaccard() to calculate the Jaccard similarity
* between all pairs of a vertex set, or \ref igraph_similarity_dice() and
* \ref igraph_similarity_dice_pairs() for a measure very similar to the
* Jaccard coefficient
*
* \example examples/simple/igraph_similarity.c
*/
int igraph_similarity_jaccard_pairs(const igraph_t *graph, igraph_vector_t *res,
const igraph_vector_t *pairs, igraph_neimode_t mode, igraph_bool_t loops) {
igraph_lazy_adjlist_t al;
long int i, j, k, u, v;
long int len_union, len_intersection;
igraph_vector_t *v1, *v2;
igraph_bool_t *seen;
k = igraph_vector_size(pairs);
if (k % 2 != 0)
IGRAPH_ERROR("number of elements in `pairs' must be even", IGRAPH_EINVAL);
IGRAPH_CHECK(igraph_vector_resize(res, k/2));
IGRAPH_CHECK(igraph_lazy_adjlist_init(graph, &al, mode, IGRAPH_SIMPLIFY));
IGRAPH_FINALLY(igraph_lazy_adjlist_destroy, &al);
if (loops) {
/* Add the loop edges */
i = igraph_vcount(graph);
seen = igraph_Calloc(i, igraph_bool_t);
if (seen == 0)
IGRAPH_ERROR("cannot calculate Jaccard similarity", IGRAPH_ENOMEM);
IGRAPH_FINALLY(free, seen);
for (i = 0; i < k; i++) {
j = (long int) VECTOR(*pairs)[i];
if (seen[j])
continue;
seen[j] = 1;
v1=igraph_lazy_adjlist_get(&al, (igraph_integer_t) j);
if (!igraph_vector_binsearch(v1, j, &u))
igraph_vector_insert(v1, u, j);
}
free(seen);
IGRAPH_FINALLY_CLEAN(1);
}
for (i = 0, j = 0; i < k; i += 2, j++) {
u = (long int) VECTOR(*pairs)[i];
v = (long int) VECTOR(*pairs)[i+1];
if (u == v) {
VECTOR(*res)[j] = 1.0;
continue;
}
v1 = igraph_lazy_adjlist_get(&al, (igraph_integer_t) u);
v2 = igraph_lazy_adjlist_get(&al, (igraph_integer_t) v);
igraph_i_neisets_intersect(v1, v2, &len_union, &len_intersection);
if (len_union > 0)
VECTOR(*res)[j] = ((igraph_real_t)len_intersection) / len_union;
else
VECTOR(*res)[j] = 0.0;
}
igraph_lazy_adjlist_destroy(&al);
IGRAPH_FINALLY_CLEAN(1);
return 0;
}
