Merge remote-tracking branch 'upstream/develop' into feat/community-detection-guide-docs

BeaMarton13 · BeaMarton13 · commit 76dfe11b8a15 · 2025-08-06T17:13:56.000+03:00
diff --git a/src/_igraph/convert.c b/src/_igraph/convert.c
@@ -374,7 +374,7 @@ int igraphmodule_PyObject_to_eigen_which_t(PyObject *o,
   w->pos = IGRAPH_EIGEN_LM;
   w->howmany = 1;
   w->il = w->iu = -1;
-  w->vl = IGRAPH_NEGINFINITY;
+  w->vl = -IGRAPH_INFINITY;
   w->vu = IGRAPH_INFINITY;
   w->vestimate = 0;
   w->balance = IGRAPH_LAPACK_DGEEVX_BALANCE_NONE;
diff --git a/src/_igraph/graphobject.c b/src/_igraph/graphobject.c
@@ -33,7 +33,6 @@
 #include "indexing.h"
 #include "memory.h"
 #include "pyhelpers.h"
-#include "utils.h"
 #include "vertexseqobject.h"
 #include <float.h>
 
@@ -5571,14 +5570,14 @@ PyObject *igraphmodule_Graph_get_shortest_path(
     return NULL;
   }
 
-  if (algorithm == IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_AUTO) {
-    algorithm = igraphmodule_select_shortest_path_algorithm(
-      &self->g, weights, NULL, mode, /* allow_johnson = */ false
-    );
-  }
-
   /* Call the C function */
   switch (algorithm) {
+    case IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_AUTO:
+      retval = igraph_get_shortest_path(
+        &self->g, weights, use_edges ? NULL : &vec, use_edges ? &vec : NULL, from, to, mode
+      );
+      break;
+
     case IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_DIJKSTRA:
       retval = igraph_get_shortest_path_dijkstra(
         &self->g, use_edges ? NULL : &vec, use_edges ? &vec : NULL, from, to, weights, mode
@@ -5793,14 +5792,15 @@ PyObject *igraphmodule_Graph_get_shortest_paths(igraphmodule_GraphObject *
     return NULL;
   }
 
-  if (algorithm == IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_AUTO) {
-    algorithm = igraphmodule_select_shortest_path_algorithm(
-      &self->g, weights, NULL, mode, /* allow_johnson = */ false
-    );
-  }
-
   /* Call the C function */
   switch (algorithm) {
+    case IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_AUTO:
+      retval = igraph_get_shortest_paths(
+        &self->g, weights, use_edges ? NULL : &veclist, use_edges ? &veclist : NULL,
+        from, to, mode, NULL, NULL
+      );
+      break;
+
     case IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_DIJKSTRA:
       retval = igraph_get_shortest_paths_dijkstra(
         &self->g, use_edges ? NULL : &veclist, use_edges ? &veclist : NULL,
@@ -6619,14 +6619,12 @@ PyObject *igraphmodule_Graph_distances(
     return igraphmodule_handle_igraph_error();
   }
 
-  if (algorithm == IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_AUTO) {
-    algorithm = igraphmodule_select_shortest_path_algorithm(
-      &self->g, weights, &from_vs, mode, /* allow_johnson = */ true
-    );
-  }
-
   /* Call the C function */
   switch (algorithm) {
+    case IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_AUTO:
+      retval = igraph_distances(&self->g, weights, &res, from_vs, to_vs, mode);
+      break;
+
     case IGRAPHMODULE_SHORTEST_PATH_ALGORITHM_DIJKSTRA:
       retval = igraph_distances_dijkstra(&self->g, &res, from_vs, to_vs, weights, mode);
       break;
@@ -13611,11 +13609,12 @@ PyObject *igraphmodule_Graph_community_walktrap(igraphmodule_GraphObject * self,
 PyObject *igraphmodule_Graph_community_leiden(igraphmodule_GraphObject *self,
         PyObject *args, PyObject *kwds) {
 
-  static char *kwlist[] = {"edge_weights", "node_weights", "resolution",
+  static char *kwlist[] = {"edge_weights", "node_weights", "node_in_weights", "resolution",
                            "normalize_resolution", "beta", "initial_membership", "n_iterations", NULL};
 
   PyObject *edge_weights_o = Py_None;
   PyObject *node_weights_o = Py_None;
+  PyObject *node_in_weights_o = Py_None;
   PyObject *initial_membership_o = Py_None;
   PyObject *normalize_resolution = Py_False;
   PyObject *res = Py_None;
@@ -13624,14 +13623,14 @@ PyObject *igraphmodule_Graph_community_leiden(igraphmodule_GraphObject *self,
   Py_ssize_t n_iterations = 2;
   double resolution = 1.0;
   double beta = 0.01;
-  igraph_vector_t *edge_weights = NULL, *node_weights = NULL;
+  igraph_vector_t *edge_weights = NULL, *node_weights = NULL, *node_in_weights = NULL;
   igraph_vector_int_t *membership = NULL;
   igraph_bool_t start = true;
   igraph_integer_t nb_clusters = 0;
   igraph_real_t quality = 0.0;
 
-  if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOdOdOn", kwlist,
-        &edge_weights_o, &node_weights_o, &resolution, &normalize_resolution, &beta, &initial_membership_o, &n_iterations))
+  if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOOdOdOn", kwlist,
+        &edge_weights_o, &node_weights_o, &node_in_weights_o, &resolution, &normalize_resolution, &beta, &initial_membership_o, &n_iterations))
     return NULL;
 
   if (n_iterations >= 0) {
@@ -13654,6 +13653,13 @@ PyObject *igraphmodule_Graph_community_leiden(igraphmodule_GraphObject *self,
     error = -1;
   }
 
+  /* Get node in-weights (directed case) */
+  if (!error && igraphmodule_attrib_to_vector_t(node_in_weights_o, self, &node_in_weights,
+    ATTRIBUTE_TYPE_VERTEX)) {
+    igraphmodule_handle_igraph_error();
+    error = -1;
+  }
+
   /* Get initial membership */
   if (!error && igraphmodule_attrib_to_vector_int_t(initial_membership_o, self, &membership,
     ATTRIBUTE_TYPE_VERTEX)) {
@@ -13664,30 +13670,35 @@ PyObject *igraphmodule_Graph_community_leiden(igraphmodule_GraphObject *self,
   if (!error && membership == 0) {
     start = 0;
     membership = (igraph_vector_int_t*)calloc(1, sizeof(igraph_vector_int_t));
-    if (membership==0) {
+    if (membership == 0) {
       PyErr_NoMemory();
       error = -1;
-    } else {
-      igraph_vector_int_init(membership, 0);
+    } else if (igraph_vector_int_init(membership, 0)) {
+      igraphmodule_handle_igraph_error();
+      error = -1;
     }
   }
 
-  if (PyObject_IsTrue(normalize_resolution))
+  if (!error && PyObject_IsTrue(normalize_resolution))
   {
     /* If we need to normalize the resolution parameter,
      * we will need to have node weights. */
     if (node_weights == 0)
     {
       node_weights = (igraph_vector_t*)calloc(1, sizeof(igraph_vector_t));
-      if (node_weights==0) {
+      if (node_weights == 0) {
         PyErr_NoMemory();
         error = -1;
-      } else {
-        igraph_vector_init(node_weights, 0);
-        if (igraph_strength(&self->g, node_weights, igraph_vss_all(), IGRAPH_ALL, 0, edge_weights)) {
-          igraphmodule_handle_igraph_error();
-          error = -1;
-        }
+      } else if (igraph_vector_init(node_weights, 0)) {
+        igraphmodule_handle_igraph_error();
+        error = -1;
+      } else if (igraph_strength(
+        &self->g, node_weights, igraph_vss_all(),
+        igraph_is_directed(&self->g) ? IGRAPH_OUT : IGRAPH_ALL,
+        IGRAPH_NO_LOOPS, edge_weights
+      )) {
+        igraphmodule_handle_igraph_error();
+        error = -1;
       }
     }
     resolution /= igraph_vector_sum(node_weights);
@@ -13696,7 +13707,7 @@ PyObject *igraphmodule_Graph_community_leiden(igraphmodule_GraphObject *self,
   /* Run actual Leiden algorithm for several iterations. */
   if (!error) {
     error = igraph_community_leiden(&self->g,
-                                    edge_weights, node_weights,
+                                    edge_weights, node_weights, node_in_weights,
                                     resolution, beta,
                                     start, n_iterations, membership,
                                     &nb_clusters, &quality);
@@ -13710,6 +13721,10 @@ PyObject *igraphmodule_Graph_community_leiden(igraphmodule_GraphObject *self,
     igraph_vector_destroy(node_weights);
     free(node_weights);
   }
+  if (node_in_weights != 0) {
+    igraph_vector_destroy(node_in_weights);
+    free(node_in_weights);
+  }
 
   if (!error && membership != 0) {
     res = igraphmodule_vector_int_t_to_PyList(membership);
diff --git a/src/_igraph/utils.c b/src/_igraph/utils.c
diff --git a/src/_igraph/utils.h b/src/_igraph/utils.h
diff --git a/src/igraph/community.py b/src/igraph/community.py
@@ -461,6 +461,7 @@ def _community_leiden(
     initial_membership=None,
     n_iterations=2,
     node_weights=None,
+    node_in_weights=None,
     **kwds,
 ):
     """Finds the community structure of the graph using the Leiden
@@ -491,6 +492,11 @@ def _community_leiden(
       If this is not provided, it will be automatically determined on the
       basis of whether you want to use CPM or modularity. If you do provide
       this, please make sure that you understand what you are doing.
+    @param node_in_weights: the inbound node weights used in the directed
+      variant of the Leiden algorithm. If this is not provided, it will be
+      automatically determined on the basis of whether you want to use CPM or
+      modularity. If you do provide this, please make sure that you understand
+      what you are doing.
     @return: an appropriate L{VertexClustering} object with an extra attribute
       called C{quality} that stores the value of the internal quality function
       optimized by the algorithm.
@@ -512,6 +518,7 @@ def _community_leiden(
         graph,
         edge_weights=weights,
         node_weights=node_weights,
+        node_in_weights=node_in_weights,
         resolution=resolution,
         normalize_resolution=(objective_function == "modularity"),
         beta=beta,
diff --git a/vendor/source/igraph b/vendor/source/igraph
@@ -1 +1 @@
-Subproject commit f858f76c2f5d2483c446ed9e8cedbc47cf74f617
+Subproject commit 2b761fa3d5fbe4dc69920597531767ffd15822e1
