diff --git a/src/konnektor/network_planners/generators/cyclic_network_generator.py b/src/konnektor/network_planners/generators/cyclic_network_generator.py
index aebf93d..b4fc3ca 100644
--- a/src/konnektor/network_planners/generators/cyclic_network_generator.py
+++ b/src/konnektor/network_planners/generators/cyclic_network_generator.py
@@ -41,7 +41,7 @@ def __init__(
         and the allowed cycle size can be tweaked with `cycle_sizes`.
 
         This layout has well-distributed connectivity between all `Component` s, which increases the robustness very well,
-        but still allows for a better graph score then the Twin Star Network, as the connectivity distribution is biased not enforced.
+        but still allows for a better graph score then the Twin Star Network, as the connectivity distribution is biased and not enforced.
         The large number of cycles might be very useful for statistical analysis.  Nevertheless, the network has an increased amount of `Transformation`s.
 
 
diff --git a/src/konnektor/network_planners/generators/redundant_minimal_spanning_tree_network_generator.py b/src/konnektor/network_planners/generators/redundant_minimal_spanning_tree_network_generator.py
index 16316f7..038812e 100644
--- a/src/konnektor/network_planners/generators/redundant_minimal_spanning_tree_network_generator.py
+++ b/src/konnektor/network_planners/generators/redundant_minimal_spanning_tree_network_generator.py
@@ -25,7 +25,7 @@ def __init__(
         robustness to `Transformation` failures in an MST-like network.
 
         This algorithm executes the MST algorithm `n_redundancy` times, always removing
-        already-selected`Transformations` in each iteration, and finally builds the overlay of all the newtorks.
+        already-selected `Transformations` in each iteration, and finally builds the overlay of all the networks.
         This is constructs the Redundant MST Network.
 
         In this way, the number of edges is increased, but the network is also less vulnerable to `Transformation` failures.