diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst
index 3e7d356464..89d9d5dfd9 100644
--- a/src/python/doc/rips_complex_user.rst
+++ b/src/python/doc/rips_complex_user.rst
@@ -54,7 +54,7 @@ regular Rips complex.
 
 Another option which is especially useful if you want to compute persistent homology in "high" dimension (2 or more,
 sometimes even 1), is to build the Rips complex only up to dimension 1 (a graph), then use
-:func:`~gudhi.SimplexTree.flag_filtration.edge_collapse.reduce_graph` to reduce the size of this graph, and finally call
+:func:`~gudhi.SimplexTree.collapse_edges` to reduce the size of this graph, and finally call
 :func:`~gudhi.SimplexTree.expansion` to get a simplicial complex of a suitable dimension to compute its homology. This
 trick gives the same persistence diagram as one would get with a plain use of `RipsComplex`, with a complex that is
 often significantly smaller and thus faster to process.
@@ -157,7 +157,7 @@ or
    cplx.expansion(2)
 
 
-This way, you can easily add a call to :func:`~gudhi.SimplexTree.flag_filtration.edge_collapse.reduce_graph` before the expansion,
+This way, you can easily add a call to :func:`~gudhi.flag_filtration.edge_collapse.reduce_graph` before the insertion,
 use a different metric to compute the matrix, or other variations.
 
 Distance matrix