add: modularity as param and conversion if needed

Author: BeaMarton13

src/_igraph/graphobject.c

@@ -13905,23 +13905,31 @@ PyObject *igraphmodule_Graph_random_walk(igraphmodule_GraphObject * self,
  */
 PyObject *igraphmodule_Graph_community_voronoi(igraphmodule_GraphObject *self,
                                                PyObject *args, PyObject *kwds) {
-    static char *kwlist[] = {"lengths", "weights", "mode", "radius", NULL};
+    static char *kwlist[] = {"modularity", "lengths", "weights", "mode", "radius", NULL};
     PyObject *lengths_o = Py_None, *weights_o = Py_None;
     PyObject *mode_o = Py_None;
     PyObject *radius_o = Py_None;
+    PyObject *modularity_o = Py_None;
     igraph_vector_t *lengths_v = NULL;
     igraph_vector_t *weights_v = NULL;
     igraph_vector_int_t membership_v, generators_v;
     igraph_neimode_t mode = IGRAPH_ALL;
     igraph_real_t radius = -1.0;  /* negative means auto-optimize */
     igraph_real_t modularity = IGRAPH_NAN;
     PyObject *membership_o, *generators_o, *result_o;
-    igraph_bool_t return_modularity = 1;
+    igraph_bool_t return_modularity = false;
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOOO", kwlist,
-                                     &lengths_o, &weights_o, &mode_o, &radius_o))
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOOOO", kwlist,
+                                     &modularity_o, &lengths_o, &weights_o, &mode_o, &radius_o))
         return NULL;
 
+    if (modularity_o != Py_None){
+      modularity = (igraph_real_t)PyFloat_AsDouble(modularity_o);
+    }
+    else {
+      return_modularity = true;
+    }
+
     /* Handle mode parameter */
     if (mode_o != Py_None) {
         if (igraphmodule_PyObject_to_neimode_t(mode_o, &mode))
@@ -18739,40 +18747,44 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "@return: the community membership vector.\n"
   },
   {"community_voronoi", 
-    (PyCFunction) igraphmodule_Graph_community_voronoi,
+    (PyCFunction) igraphmodule_Graph_community_voronoi, 
     METH_VARARGS | METH_KEYWORDS,
-    "community_voronoi(lengths=None, weights=None, mode=\"all\", radius=None)\n\n"
-    "Finds communities using Voronoi partitioning.\n\n"
-    "This function finds communities using a Voronoi partitioning of vertices based\n"
-    "on the given edge lengths divided by the edge clustering coefficient.\n"
-    "The generator vertices are chosen to be those with the largest local relative\n"
-    "density within a radius, with the local relative density of a vertex defined as\n"
-    "s * m / (m + k), where s is the strength of the vertex, m is the number of\n"
-    "edges within the vertex's first order neighborhood, while k is the number of\n"
-    "edges with only one endpoint within this neighborhood.\n\n"
-    "@param lengths: edge lengths, or C{None} to consider all edges as having\n"
-    "  unit length. Voronoi partitioning will use edge lengths equal to\n"
-    "  lengths / ECC where ECC is the edge clustering coefficient.\n"
-    "@param weights: edge weights, or C{None} to consider all edges as having\n"
-    "  unit weight. Weights are used when selecting generator points, as well\n"
-    "  as for computing modularity.\n"
-    "@param mode: if C{\"out\"}, distances from generator points to all other\n"
-    "  nodes are considered. If C{\"in\"}, the reverse distances are used.\n"
-    "  If C{\"all\"}, edge directions are ignored. This parameter is ignored\n"
-    "  for undirected graphs.\n"
-    "@param radius: the radius/resolution to use when selecting generator points.\n"
-    "  The larger this value, the fewer partitions there will be. Pass C{None}\n"
-    "  to automatically select the radius that maximizes modularity.\n"
-    "@return: a tuple containing the membership vector, generator vertices,\n"
-    "  and modularity score.\n"
-    "@rtype: tuple\n\n"
-    "@newfield ref: Reference\n"
-    "@ref: Deritei et al., Community detection by graph Voronoi diagrams,\n"
-    "  New Journal of Physics 16, 063007 (2014)\n"
-    "  U{https://doi.org/10.1088/1367-2630/16/6/063007}\n"
-    "@ref: Molnár et al., Community Detection in Directed Weighted Networks\n"
-    "  using Voronoi Partitioning, Scientific Reports 14, 8124 (2024)\n"
-    "  U{https://doi.org/10.1038/s41598-024-58624-4}\n"
+   "community_voronoi(lengths=None, weights=None, mode=\"all\", radius=None, modularity=None)\n\n"
+   "Finds communities using Voronoi partitioning.\n\n"
+   "This function finds communities using a Voronoi partitioning of vertices based\n"
+   "on the given edge lengths divided by the edge clustering coefficient.\n"
+   "The generator vertices are chosen to be those with the largest local relative\n"
+   "density within a radius, with the local relative density of a vertex defined as\n"
+   "s * m / (m + k), where s is the strength of the vertex, m is the number of\n"
+   "edges within the vertex's first order neighborhood, while k is the number of\n"
+   "edges with only one endpoint within this neighborhood.\n\n"
+   "@param lengths: edge lengths, or C{None} to consider all edges as having\n"
+   "  unit length. Voronoi partitioning will use edge lengths equal to\n"
+   "  lengths / ECC where ECC is the edge clustering coefficient.\n"
+   "@param weights: edge weights, or C{None} to consider all edges as having\n"
+   "  unit weight. Weights are used when selecting generator points, as well\n"
+   "  as for computing modularity.\n"
+   "@param mode: if C{\"out\"}, distances from generator points to all other\n"
+   "  nodes are considered. If C{\"in\"}, the reverse distances are used.\n"
+   "  If C{\"all\"}, edge directions are ignored. This parameter is ignored\n"
+   "  for undirected graphs.\n"
+   "@param radius: the radius/resolution to use when selecting generator points.\n"
+   "  The larger this value, the fewer partitions there will be. Pass C{None}\n"
+   "  to automatically select the radius that maximizes modularity.\n"
+   "@param modularity: if not C{None}, the modularity score will be calculated\n"
+   "  and returned as part of the result tuple.\n"
+   "@return: a tuple containing the membership vector and generator vertices.\n"
+   "  When modularity calculation is requested, also includes the modularity score\n"
+   "  as a third element: (membership, generators, modularity).\n"
+   "  Otherwise: (membership, generators).\n"
+   "@rtype: tuple\n\n"
+   "B{References}\n\n"
+    "  - Deritei et al., Community detection by graph Voronoi diagrams,\n"
+    "    New Journal of Physics 16, 063007 (2014)\n"
+    "    https://doi.org/10.1088/1367-2630/16/6/063007\n"
+    "  - Molnár et al., Community Detection in Directed Weighted Networks\n"
+    "    using Voronoi Partitioning, Scientific Reports 14, 8124 (2024)\n"
+    "    https://doi.org/10.1038/s41598-024-58624-4\n"
   },
   {"community_leiden",
    (PyCFunction) igraphmodule_Graph_community_leiden,

src/igraph/community.py

@@ -320,7 +320,7 @@ def _community_spinglass(graph, *args, **kwds):
     return VertexClustering(graph, membership, modularity_params=modularity_params)
 
 
-def _community_voronoi(graph, lengths=None, weights=None, mode="all", radius=None):
+def _community_voronoi(graph, modularity=None, lengths=None, weights=None, mode="all", radius=None):
     """Finds communities using Voronoi partitioning.
 
     This function finds communities using a Voronoi partitioning of vertices based
@@ -364,8 +364,11 @@ def _community_voronoi(graph, lengths=None, weights=None, mode="all", radius=Non
             mode = mode_map[mode.lower()]
         else:
             raise ValueError(f"Invalid mode '{mode}'. Must be one of: out, in, all")
-
-    membership, generators, modularity = GraphBase.community_voronoi(graph, lengths, weights, mode, radius)
+    
+    if modularity is None:
+      membership, generators, modularity = GraphBase.community_voronoi(graph, modularity, lengths, weights, mode, radius)
+    else:
+      membership, generators = GraphBase.community_voronoi(graph, modularity, lengths, weights, mode, radius)
 
     params = {"generators": generators}
     modularity_params = {}