diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py
index 01885c2d1c3..fa94fa67625 100644
--- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py
+++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py
@@ -14,7 +14,6 @@
 import gc
 from typing import Union
 import warnings
-import random
 
 import cudf
 import cupy as cp
@@ -182,10 +181,7 @@ def __from_edgelist(
         workers = _client.scheduler_info()["workers"]
         # Repartition to 2 partitions per GPU for memory efficient process
         input_ddf = input_ddf.repartition(npartitions=len(workers) * 2)
-        # FIXME: Make a copy of the input ddf before implicitly altering it.
-        input_ddf = input_ddf.map_partitions(
-            lambda df: df.copy(), token="custom-" + str(random.random())
-        )
+        input_ddf = input_ddf.map_partitions(lambda df: df.copy())
         # The dataframe will be symmetrized iff the graph is undirected
         # otherwise, the inital dataframe will be returned
         if edge_attr is not None:
@@ -337,7 +333,7 @@ def __from_edgelist(
             )
             for w, edata in ddf.items()
         }
-        del ddf
+        # FIXME: For now, don't delete the copied dataframe to avoid crash
         self._plc_graph = {
             w: _client.compute(delayed_task, workers=w, allow_other_workers=False)
             for w, delayed_task in delayed_tasks_d.items()
@@ -1196,7 +1192,5 @@ def _get_column_from_ls_dfs(lst_df, col_name):
     if len_df == 0:
         return lst_df[0][col_name]
     output_col = cudf.concat([df[col_name] for df in lst_df], ignore_index=True)
-    for df in lst_df:
-        df.drop(columns=[col_name], inplace=True)
-    gc.collect()
+    # FIXME: For now, don't delete the copied dataframe to avoid cras
     return output_col