diff --git a/src/graphs/__init__.py b/src/graphs/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/graphs/algorithms.py b/src/graphs/algorithms.py
new file mode 100644
index 00000000..2272565d
--- /dev/null
+++ b/src/graphs/algorithms.py
@@ -0,0 +1,40 @@
+"""
+Module that contains useful methods for traversing graphs
+"""
+from typing import Tuple, List
+import networkx as nx
+
+
+def condense_to_dag(g: nx.Graph) -> Tuple[nx.DiGraph, List[List]]:
+    """
+    Given a graph g, returns another graph such that all the nodes that form a cycle in
+    the original graph are condense into a single node.
+
+    Returns
+    -------
+        dag: tuple
+            a tuple with the directed graph that subsumes the new information and
+            a list with the corresponding nodes in the original graph for each connected component
+    """
+
+    # Get the strongly connected components (SCCs)
+    sccs = list(nx.strongly_connected_components(g))
+
+    # Create a new directed graph for the DAG
+    dag = nx.DiGraph()
+
+    # Map each node to its SCC
+    scc_map = {}
+    for i, scc in enumerate(sccs):
+        for node in scc:
+            scc_map[node] = i
+
+    # Add nodes to the DAG, each node represents an SCC
+    dag.add_nodes_from(range(len(sccs)))
+
+    # Add edges between SCCs to form the DAG
+    for u, v in g.edges():
+        if scc_map[u] != scc_map[v]:
+            dag.add_edge(scc_map[u], scc_map[v])
+
+    return dag, sccs
diff --git a/src/liveness/liveness_analysis.py b/src/liveness/liveness_analysis.py
index 2fc0a2e2..13fca206 100644
--- a/src/liveness/liveness_analysis.py
+++ b/src/liveness/liveness_analysis.py
@@ -7,6 +7,7 @@
 from analysis.fixpoint_analysis import BlockAnalysisInfo, BackwardsAnalysis
 from analysis.abstract_state import digraph_from_block_info
 from liveness.liveness_state import LivenessState, LivenessBlockInfo
+from graphs.algorithms import condense_to_dag
 from parser.cfg_block_list import CFGBlockList
 from parser.cfg import CFG
 
@@ -137,4 +138,15 @@ def dot_from_analysis(cfg: CFG, final_dir: Path = Path(".")) -> Dict[str, Dict[s
         renamed_digraph = nx.relabel_nodes(digraph, renaming_dict)
 
         nx.nx_agraph.write_dot(renamed_digraph, final_dir.joinpath(f"{component_name}.dot"))
+
+        condensed_digraph, sccs = condense_to_dag(renamed_digraph)
+
+        # We just consider the first part of the dot representation ("Block {i}:")
+        renamed_condensed_dict = {i: '\n'.join(sorted([dot_repr.splitlines()[0] for dot_repr in dot_repr_from_variables]))
+                                  for i, dot_repr_from_variables in enumerate(sccs)}
+
+        renamed_condensed_digraph = nx.relabel_nodes(condensed_digraph, renamed_condensed_dict)
+
+        nx.nx_agraph.write_dot(renamed_condensed_digraph, final_dir.joinpath(f"{component_name}_condensed.dot"))
+
     return results