def _stabilize_graph(graph: nx.Graph) -> nx.Graph:
    """Ensure an undirected graph with the same relationships will always be read the same way."""
    fixed_graph = nx.DiGraph() if graph.is_directed() else nx.Graph()

    sorted_nodes = graph.nodes(data=True)
    sorted_nodes = sorted(sorted_nodes, key=lambda x: x[0])

    fixed_graph.add_nodes_from(sorted_nodes)
    edges = list(graph.edges(data=True))

    # If the graph is undirected, we create the edges in a stable way, so we get the same results
    # for example:
    # A -> B
    # in graph theory is the same as
    # B -> A
    # in an undirected graph
    # however, this can lead to downstream issues because sometimes
    # consumers read graph.nodes() which ends up being [A, B] and sometimes it's [B, A]
    # but they base some of their logic on the order of the nodes, so the order ends up being important
    # so we sort the nodes in the edge in a stable way, so that we always get the same order
    if not graph.is_directed():

        def _sort_source_target(edge):
            source, target, edge_data = edge
            if source > target:
                temp = source
                source = target
                target = temp
            return source, target, edge_data

        edges = [_sort_source_target(edge) for edge in edges]

    def _get_edge_key(source: Any, target: Any) -> str:
        return f"{source} -> {target}"

    edges = sorted(edges, key=lambda x: _get_edge_key(x[0], x[1]))

    fixed_graph.add_edges_from(edges)
    return fixed_graph
Ensure an undirected graph with the same relationships will always be read the same way.
graphrag/general/leiden.py