diff --git a/compare_graph.py b/compare_graph.py
new file mode 100644
index 0000000..4c7b143
--- /dev/null
+++ b/compare_graph.py
@@ -0,0 +1,210 @@
+import pypsa
+import networkx as nx
+import matplotlib.pyplot as plt
+
+
+def graph_properties(network: pypsa.Network) -> dict:
+    """
+    Extracts various properties of the provided PyPSA network object.
+
+    Args:
+        network (pypsa.Network): The PyPSA network object to analyze.
+
+    Returns:
+        dict: A dictionary containing the extracted network properties.
+    """
+
+    # Get the underlying NetworkX graph object from the PyPSA network
+    graph = network.graph()
+
+    # Extract degree information for each node (node and its connected edges)
+    network_degree = dict(graph.degree())
+
+    # Calculate the average degree of connectivity in the network
+    network_average_degree_connectivity = nx.average_degree_connectivity(graph)
+
+    # Extract a list of edges and nodes from the NetworkX graph
+    network_edges = list(graph.edges())
+    network_nodes = list(graph.nodes())
+
+    # Get the total number of edges and nodes in the network
+    network_number_of_edges = graph.number_of_edges()
+    network_number_of_nodes = graph.number_of_nodes()
+
+    # Create a dictionary to store the extracted network properties
+    network_properties = {
+        "network_edges": network_edges,
+        "network_degree": network_degree,
+        "network_average_degree_connectivity": network_average_degree_connectivity,
+        "network_nodes": network_nodes,
+        "network_number_of_edges": network_number_of_edges,
+        "network_number_of_nodes": network_number_of_nodes,
+    }
+
+    return network_properties
+
+
+def plot_network_graph(network: pypsa.Network, seed: int = 1969) -> None:
+    """
+    Plots the network graph.
+
+    Args:
+        network: pypsa.Network
+    Returns:
+        None
+    """
+    graph = network.graph()
+    options = {"node_color": "black", "node_size": 50,
+               "linewidths": 0, "width": 0.1}
+    pos = nx.spring_layout(graph, seed=seed)  # Seed for reproducible layout
+    nx.draw(graph, pos, **options)
+    plt.savefig("network_graph.png", dpi=300)
+
+
+def available_item_properties(
+    network1: pypsa.Network, network2: pypsa.Network, item: str
+) -> list:
+    """
+    Identifies items present in both network1 and network2 for the specified item type.
+
+    Args:
+        network1 (pypsa.Network): The first network to compare.
+        network2 (pypsa.Network): The second network to compare.
+        item (str): The type of item to check for availability, either "network_nodes" or "network_edges".
+
+    Returns:
+        list: A list of items present in both network1 and network2.
+    """
+
+    if item not in ["network_nodes", "network_edges"]:  # Validate item type
+        raise ValueError(
+            "Invalid item type. Must be 'network_nodes' or 'network_edges'."
+        )
+
+    available_item = []  # Initialize a list to store common items
+
+    # Iterate through items in network1 and check for presence in network2
+    for idx in network1[item]:
+        if idx in network2[item]:
+            available_item.append(idx)
+
+    return available_item
+
+
+def missing_item_properties(
+    network1: pypsa.Network, network2: pypsa.Network, item: str
+) -> list:
+    """
+    Identifies items present in network1 but missing in network2 for the specified item type.
+
+    Args:
+        network1 (pypsa.Network): The first network to compare.
+        network2 (pypsa.Network): The second network to compare.
+        item (str): The type of item to check for missing entries, either "network_nodes" or "network_edges".
+
+    Returns:
+        list: A list of items present in network1 but missing in network2.
+    """
+
+    if item not in ["network_nodes", "network_edges"]:  # Validate item type
+        raise ValueError(
+            "Invalid item type. Must be 'network_nodes' or 'network_edges'."
+        )
+
+    missing_item = []  # Initialize a list to store missing items
+
+    # Iterate through items in network1 and identify those missing in network2
+    for idx in network1[item]:
+        if idx not in network2[item]:
+            missing_item.append(idx)
+
+    return missing_item
+
+
+def compare_graph_properties(network1: pypsa.Network, network2: pypsa.Network) -> dict:
+    """
+    This function compares the properties of two PyPSA network objects.
+
+    Args:
+            network1 (pypsa.Network): The first network to compare.
+            network2 (pypsa.Network): The second network to compare.
+
+    Returns:
+            dict: A dictionary containing the similarities and differences between the networks.
+    """
+
+    network1_properties = graph_properties(
+        network1)  # Get properties of network 1
+    network2_properties = graph_properties(
+        network2)  # Get properties of network 2
+
+    difference_dict = {}  # Dictionary to store property differences
+    similar_dict = {}  # Dictionary to store property similarities
+
+    results = {"similarities": similar_dict, "differences": difference_dict}
+
+    # Find common properties (edges & nodes) present in both networks
+    similar_dict["edges"] = available_item_properties(
+        network1_properties, network2_properties, "network_edges"
+    )
+    similar_dict["nodes"] = available_item_properties(
+        network1_properties, network2_properties, "network_nodes"
+    )
+
+    # Calculate the absolute difference in the number of edges and nodes
+    difference_dict["edge_difference"] = abs(
+        network1_properties["network_number_of_edges"]
+        - network2_properties["network_number_of_edges"]
+    )
+    difference_dict["node_difference"] = abs(
+        network1_properties["network_number_of_nodes"]
+        - network2_properties["network_number_of_nodes"]
+    )
+
+    # Find missing edges (those in network 1 but not in network 2 and vice versa)
+    edges_in_n1_not_in_n2 = missing_item_properties(
+        network1_properties, network2_properties, "network_edges"
+    )
+    edges_in_n2_not_in_n1 = missing_item_properties(
+        network2_properties, network1_properties, "network_edges"
+    )
+
+    # Find missing nodes (those in network 1 but not in network 2 and vice versa)
+    nodes_in_n1_not_in_n2 = missing_item_properties(
+        network1_properties, network2_properties, "network_nodes"
+    )
+    nodes_in_n2_not_in_n1 = missing_item_properties(
+        network2_properties, network1_properties, "network_nodes"
+    )
+
+    difference_dict["edges_in_n1_not_in_n2"] = edges_in_n1_not_in_n2
+    difference_dict["edges_in_n2_not_in_n1"] = edges_in_n2_not_in_n1
+    difference_dict["nodes_in_n1_not_in_n2"] = nodes_in_n1_not_in_n2
+    difference_dict["nodes_in_n2_not_in_n1"] = nodes_in_n2_not_in_n1
+
+    degree = {}  # Dictionary to store degree information
+
+    # Calculate average degree for each network
+    average_network_degree = pd.DataFrame(
+        [
+            network1_properties["network_average_degree_connectivity"],
+            network2_properties["network_average_degree_connectivity"],
+        ],
+        index=["network_1", "network_2"],
+    ).T
+    degree["average_degree"] = average_network_degree.sort_index(inplace=True)
+
+    # Combine degree information for both networks into a DataFrame
+    network_degree = pd.DataFrame(
+        [network1_properties["network_degree"],
+            network2_properties["network_degree"]]
+    ).T
+    network_degree.rename(
+        columns={"0": "network_1", "1": "network_2"}, inplace=True)
+
+    difference_dict["degree"] = {
+        "network_degree": network_degree,
+        "average_network_degree": average_network_degree,
+    }
+
+    return results