# Source code for networkx.algorithms.dag

```
"""Algorithms for directed acyclic graphs (DAGs).
Note that most of these functions are only guaranteed to work for DAGs.
In general, these functions do not check for acyclic-ness, so it is up
to the user to check for that.
"""
from collections import deque
from math import gcd
from functools import partial
from itertools import chain, product, starmap
import heapq
import networkx as nx
from networkx.utils import arbitrary_element, pairwise, not_implemented_for
__all__ = [
"descendants",
"ancestors",
"topological_sort",
"lexicographical_topological_sort",
"all_topological_sorts",
"topological_generations",
"is_directed_acyclic_graph",
"is_aperiodic",
"transitive_closure",
"transitive_closure_dag",
"transitive_reduction",
"antichains",
"dag_longest_path",
"dag_longest_path_length",
"dag_to_branching",
]
chaini = chain.from_iterable
[docs]def descendants(G, source):
"""Returns all nodes reachable from `source` in `G`.
Parameters
----------
G : NetworkX DiGraph
A directed graph
source : node in `G`
Returns
-------
set()
The descendants of `source` in `G`
"""
if not G.has_node(source):
raise nx.NetworkXError(f"The node {source} is not in the graph.")
des = {n for n, d in nx.shortest_path_length(G, source=source).items()}
return des - {source}
[docs]def ancestors(G, source):
"""Returns all nodes having a path to `source` in `G`.
Parameters
----------
G : NetworkX DiGraph
A directed graph
source : node in `G`
Returns
-------
set()
The ancestors of source in G
"""
if not G.has_node(source):
raise nx.NetworkXError(f"The node {source} is not in the graph.")
anc = {n for n, d in nx.shortest_path_length(G, target=source).items()}
return anc - {source}
def has_cycle(G):
"""Decides whether the directed graph has a cycle."""
try:
# Feed the entire iterator into a zero-length deque.
deque(topological_sort(G), maxlen=0)
except nx.NetworkXUnfeasible:
return True
else:
return False
[docs]def is_directed_acyclic_graph(G):
"""Returns True if the graph `G` is a directed acyclic graph (DAG) or
False if not.
Parameters
----------
G : NetworkX graph
Returns
-------
bool
True if `G` is a DAG, False otherwise
"""
return G.is_directed() and not has_cycle(G)
[docs]def topological_generations(G):
"""Stratifies a DAG into generations.
A topological generation is node collection in which ancestors of a node in each
generation are guaranteed to be in a previous generation, and any descendants of
a node are guaranteed to be in a following generation. Nodes are guaranteed to
be in the earliest possible generation that they can belong to.
Parameters
----------
G : NetworkX digraph
A directed acyclic graph (DAG)
Yields
------
sets of nodes
Yields sets of nodes representing each generation.
Raises
------
NetworkXError
Generations are defined for directed graphs only. If the graph
`G` is undirected, a :exc:`NetworkXError` is raised.
NetworkXUnfeasible
If `G` is not a directed acyclic graph (DAG) no topological generations
exist and a :exc:`NetworkXUnfeasible` exception is raised. This can also
be raised if `G` is changed while the returned iterator is being processed
RuntimeError
If `G` is changed while the returned iterator is being processed.
Examples
--------
>>> DG = nx.DiGraph([(2, 1), (3, 1)])
>>> [sorted(generation) for generation in nx.topological_generations(DG)]
[[2, 3], [1]]
Notes
-----
The generation in which a node resides can also be determined by taking the
max-path-distance from the node to the farthest leaf node. That value can
be obtained with this function using `enumerate(topological_generations(G))`.
See also
--------
topological_sort
"""
if not G.is_directed():
raise nx.NetworkXError("Topological sort not defined on undirected graphs.")
multigraph = G.is_multigraph()
indegree_map = {v: d for v, d in G.in_degree() if d > 0}
zero_indegree = [v for v, d in G.in_degree() if d == 0]
while zero_indegree:
this_generation = zero_indegree
zero_indegree = []
for node in this_generation:
if node not in G:
raise RuntimeError("Graph changed during iteration")
for child in G.neighbors(node):
try:
indegree_map[child] -= len(G[node][child]) if multigraph else 1
except KeyError as e:
raise RuntimeError("Graph changed during iteration") from e
if indegree_map[child] == 0:
zero_indegree.append(child)
del indegree_map[child]
yield this_generation
if indegree_map:
raise nx.NetworkXUnfeasible(
"Graph contains a cycle or graph changed during iteration"
)
[docs]def topological_sort(G):
"""Returns a generator of nodes in topologically sorted order.
A topological sort is a nonunique permutation of the nodes of a
directed graph such that an edge from u to v implies that u
appears before v in the topological sort order. This ordering is
valid only if the graph has no directed cycles.
Parameters
----------
G : NetworkX digraph
A directed acyclic graph (DAG)
Yields
------
nodes
Yields the nodes in topological sorted order.
Raises
------
NetworkXError
Topological sort is defined for directed graphs only. If the graph `G`
is undirected, a :exc:`NetworkXError` is raised.
NetworkXUnfeasible
If `G` is not a directed acyclic graph (DAG) no topological sort exists
and a :exc:`NetworkXUnfeasible` exception is raised. This can also be
raised if `G` is changed while the returned iterator is being processed
RuntimeError
If `G` is changed while the returned iterator is being processed.
Examples
--------
To get the reverse order of the topological sort:
>>> DG = nx.DiGraph([(1, 2), (2, 3)])
>>> list(reversed(list(nx.topological_sort(DG))))
[3, 2, 1]
If your DiGraph naturally has the edges representing tasks/inputs
and nodes representing people/processes that initiate tasks, then
topological_sort is not quite what you need. You will have to change
the tasks to nodes with dependence reflected by edges. The result is
a kind of topological sort of the edges. This can be done
with :func:`networkx.line_graph` as follows:
>>> list(nx.topological_sort(nx.line_graph(DG)))
[(1, 2), (2, 3)]
Notes
-----
This algorithm is based on a description and proof in
"Introduction to Algorithms: A Creative Approach" [1]_ .
See also
--------
is_directed_acyclic_graph, lexicographical_topological_sort
References
----------
.. [1] Manber, U. (1989).
*Introduction to Algorithms - A Creative Approach.* Addison-Wesley.
"""
for generation in nx.topological_generations(G):
yield from generation
[docs]def lexicographical_topological_sort(G, key=None):
"""Returns a generator of nodes in lexicographically topologically sorted
order.
A topological sort is a nonunique permutation of the nodes such that an
edge from u to v implies that u appears before v in the topological sort
order.
Parameters
----------
G : NetworkX digraph
A directed acyclic graph (DAG)
key : function, optional
This function maps nodes to keys with which to resolve ambiguities in
the sort order. Defaults to the identity function.
Returns
-------
iterable
An iterable of node names in lexicographical topological sort order.
Raises
------
NetworkXError
Topological sort is defined for directed graphs only. If the graph `G`
is undirected, a :exc:`NetworkXError` is raised.
NetworkXUnfeasible
If `G` is not a directed acyclic graph (DAG) no topological sort exists
and a :exc:`NetworkXUnfeasible` exception is raised. This can also be
raised if `G` is changed while the returned iterator is being processed
RuntimeError
If `G` is changed while the returned iterator is being processed.
Notes
-----
This algorithm is based on a description and proof in
"Introduction to Algorithms: A Creative Approach" [1]_ .
See also
--------
topological_sort
References
----------
.. [1] Manber, U. (1989).
*Introduction to Algorithms - A Creative Approach.* Addison-Wesley.
"""
if not G.is_directed():
msg = "Topological sort not defined on undirected graphs."
raise nx.NetworkXError(msg)
if key is None:
def key(node):
return node
nodeid_map = {n: i for i, n in enumerate(G)}
def create_tuple(node):
return key(node), nodeid_map[node], node
indegree_map = {v: d for v, d in G.in_degree() if d > 0}
# These nodes have zero indegree and ready to be returned.
zero_indegree = [create_tuple(v) for v, d in G.in_degree() if d == 0]
heapq.heapify(zero_indegree)
while zero_indegree:
_, _, node = heapq.heappop(zero_indegree)
if node not in G:
raise RuntimeError("Graph changed during iteration")
for _, child in G.edges(node):
try:
indegree_map[child] -= 1
except KeyError as e:
raise RuntimeError("Graph changed during iteration") from e
if indegree_map[child] == 0:
heapq.heappush(zero_indegree, create_tuple(child))
del indegree_map[child]
yield node
if indegree_map:
msg = "Graph contains a cycle or graph changed during iteration"
raise nx.NetworkXUnfeasible(msg)
[docs]@not_implemented_for("undirected")
def all_topological_sorts(G):
"""Returns a generator of _all_ topological sorts of the directed graph G.
A topological sort is a nonunique permutation of the nodes such that an
edge from u to v implies that u appears before v in the topological sort
order.
Parameters
----------
G : NetworkX DiGraph
A directed graph
Returns
-------
generator
All topological sorts of the digraph G
Raises
------
NetworkXNotImplemented
If `G` is not directed
NetworkXUnfeasible
If `G` is not acyclic
Examples
--------
To enumerate all topological sorts of directed graph:
>>> DG = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
>>> list(nx.all_topological_sorts(DG))
[[1, 2, 4, 3], [1, 2, 3, 4]]
Notes
-----
Implements an iterative version of the algorithm given in [1].
References
----------
.. [1] Knuth, Donald E., Szwarcfiter, Jayme L. (1974).
"A Structured Program to Generate All Topological Sorting Arrangements"
Information Processing Letters, Volume 2, Issue 6, 1974, Pages 153-157,
ISSN 0020-0190,
https://doi.org/10.1016/0020-0190(74)90001-5.
Elsevier (North-Holland), Amsterdam
"""
if not G.is_directed():
raise nx.NetworkXError("Topological sort not defined on undirected graphs.")
# the names of count and D are chosen to match the global variables in [1]
# number of edges originating in a vertex v
count = dict(G.in_degree())
# vertices with indegree 0
D = deque([v for v, d in G.in_degree() if d == 0])
# stack of first value chosen at a position k in the topological sort
bases = []
current_sort = []
# do-while construct
while True:
assert all([count[v] == 0 for v in D])
if len(current_sort) == len(G):
yield list(current_sort)
# clean-up stack
while len(current_sort) > 0:
assert len(bases) == len(current_sort)
q = current_sort.pop()
# "restores" all edges (q, x)
# NOTE: it is important to iterate over edges instead
# of successors, so count is updated correctly in multigraphs
for _, j in G.out_edges(q):
count[j] += 1
assert count[j] >= 0
# remove entries from D
while len(D) > 0 and count[D[-1]] > 0:
D.pop()
# corresponds to a circular shift of the values in D
# if the first value chosen (the base) is in the first
# position of D again, we are done and need to consider the
# previous condition
D.appendleft(q)
if D[-1] == bases[-1]:
# all possible values have been chosen at current position
# remove corresponding marker
bases.pop()
else:
# there are still elements that have not been fixed
# at the current position in the topological sort
# stop removing elements, escape inner loop
break
else:
if len(D) == 0:
raise nx.NetworkXUnfeasible("Graph contains a cycle.")
# choose next node
q = D.pop()
# "erase" all edges (q, x)
# NOTE: it is important to iterate over edges instead
# of successors, so count is updated correctly in multigraphs
for _, j in G.out_edges(q):
count[j] -= 1
assert count[j] >= 0
if count[j] == 0:
D.append(j)
current_sort.append(q)
# base for current position might _not_ be fixed yet
if len(bases) < len(current_sort):
bases.append(q)
if len(bases) == 0:
break
[docs]def is_aperiodic(G):
"""Returns True if `G` is aperiodic.
A directed graph is aperiodic if there is no integer k > 1 that
divides the length of every cycle in the graph.
Parameters
----------
G : NetworkX DiGraph
A directed graph
Returns
-------
bool
True if the graph is aperiodic False otherwise
Raises
------
NetworkXError
If `G` is not directed
Notes
-----
This uses the method outlined in [1]_, which runs in $O(m)$ time
given $m$ edges in `G`. Note that a graph is not aperiodic if it is
acyclic as every integer trivial divides length 0 cycles.
References
----------
.. [1] Jarvis, J. P.; Shier, D. R. (1996),
"Graph-theoretic analysis of finite Markov chains,"
in Shier, D. R.; Wallenius, K. T., Applied Mathematical Modeling:
A Multidisciplinary Approach, CRC Press.
"""
if not G.is_directed():
raise nx.NetworkXError("is_aperiodic not defined for undirected graphs")
s = arbitrary_element(G)
levels = {s: 0}
this_level = [s]
g = 0
lev = 1
while this_level:
next_level = []
for u in this_level:
for v in G[u]:
if v in levels: # Non-Tree Edge
g = gcd(g, levels[u] - levels[v] + 1)
else: # Tree Edge
next_level.append(v)
levels[v] = lev
this_level = next_level
lev += 1
if len(levels) == len(G): # All nodes in tree
return g == 1
else:
return g == 1 and nx.is_aperiodic(G.subgraph(set(G) - set(levels)))
[docs]@not_implemented_for("undirected")
def transitive_closure(G, reflexive=False):
"""Returns transitive closure of a directed graph
The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that
for all v, w in V there is an edge (v, w) in E+ if and only if there
is a path from v to w in G.
Handling of paths from v to v has some flexibility within this definition.
A reflexive transitive closure creates a self-loop for the path
from v to v of length 0. The usual transitive closure creates a
self-loop only if a cycle exists (a path from v to v with length > 0).
We also allow an option for no self-loops.
Parameters
----------
G : NetworkX DiGraph
A directed graph
reflexive : Bool or None, optional (default: False)
Determines when cycles create self-loops in the Transitive Closure.
If True, trivial cycles (length 0) create self-loops. The result
is a reflexive tranistive closure of G.
If False (the default) non-trivial cycles create self-loops.
If None, self-loops are not created.
Returns
-------
NetworkX DiGraph
The transitive closure of `G`
Raises
------
NetworkXNotImplemented
If `G` is not directed
References
----------
.. [1] http://www.ics.uci.edu/~eppstein/PADS/PartialOrder.py
TODO this function applies to all directed graphs and is probably misplaced
here in dag.py
"""
if reflexive is None:
TC = G.copy()
for v in G:
edges = ((v, u) for u in nx.dfs_preorder_nodes(G, v) if v != u)
TC.add_edges_from(edges)
return TC
if reflexive is True:
TC = G.copy()
for v in G:
edges = ((v, u) for u in nx.dfs_preorder_nodes(G, v))
TC.add_edges_from(edges)
return TC
# reflexive is False
TC = G.copy()
for v in G:
edges = ((v, w) for u, w in nx.edge_dfs(G, v))
TC.add_edges_from(edges)
return TC
[docs]@not_implemented_for("undirected")
def transitive_closure_dag(G, topo_order=None):
"""Returns the transitive closure of a directed acyclic graph.
This function is faster than the function `transitive_closure`, but fails
if the graph has a cycle.
The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that
for all v, w in V there is an edge (v, w) in E+ if and only if there
is a non-null path from v to w in G.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
topo_order: list or tuple, optional
A topological order for G (if None, the function will compute one)
Returns
-------
NetworkX DiGraph
The transitive closure of `G`
Raises
------
NetworkXNotImplemented
If `G` is not directed
NetworkXUnfeasible
If `G` has a cycle
Notes
-----
This algorithm is probably simple enough to be well-known but I didn't find
a mention in the literature.
"""
if topo_order is None:
topo_order = list(topological_sort(G))
TC = G.copy()
# idea: traverse vertices following a reverse topological order, connecting
# each vertex to its descendants at distance 2 as we go
for v in reversed(topo_order):
TC.add_edges_from((v, u) for u in nx.descendants_at_distance(TC, v, 2))
return TC
[docs]@not_implemented_for("undirected")
def transitive_reduction(G):
"""Returns transitive reduction of a directed graph
The transitive reduction of G = (V,E) is a graph G- = (V,E-) such that
for all v,w in V there is an edge (v,w) in E- if and only if (v,w) is
in E and there is no path from v to w in G with length greater than 1.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
Returns
-------
NetworkX DiGraph
The transitive reduction of `G`
Raises
------
NetworkXError
If `G` is not a directed acyclic graph (DAG) transitive reduction is
not uniquely defined and a :exc:`NetworkXError` exception is raised.
Examples
--------
To perform transitive reduction on a DiGraph:
>>> DG = nx.DiGraph([(1, 2), (2, 3), (1, 3)])
>>> TR = nx.transitive_reduction(DG)
>>> list(TR.edges)
[(1, 2), (2, 3)]
To avoid unnecessary data copies, this implementation does not return a
DiGraph with node/edge data.
To perform transitive reduction on a DiGraph and transfer node/edge data:
>>> DG = nx.DiGraph()
>>> DG.add_edges_from([(1, 2), (2, 3), (1, 3)], color='red')
>>> TR = nx.transitive_reduction(DG)
>>> TR.add_nodes_from(DG.nodes(data=True))
>>> TR.add_edges_from((u, v, DG.edges[u, v]) for u, v in TR.edges)
>>> list(TR.edges(data=True))
[(1, 2, {'color': 'red'}), (2, 3, {'color': 'red'})]
References
----------
https://en.wikipedia.org/wiki/Transitive_reduction
"""
if not is_directed_acyclic_graph(G):
msg = "Directed Acyclic Graph required for transitive_reduction"
raise nx.NetworkXError(msg)
TR = nx.DiGraph()
TR.add_nodes_from(G.nodes())
descendants = {}
# count before removing set stored in descendants
check_count = dict(G.in_degree)
for u in G:
u_nbrs = set(G[u])
for v in G[u]:
if v in u_nbrs:
if v not in descendants:
descendants[v] = {y for x, y in nx.dfs_edges(G, v)}
u_nbrs -= descendants[v]
check_count[v] -= 1
if check_count[v] == 0:
del descendants[v]
TR.add_edges_from((u, v) for v in u_nbrs)
return TR
[docs]@not_implemented_for("undirected")
def antichains(G, topo_order=None):
"""Generates antichains from a directed acyclic graph (DAG).
An antichain is a subset of a partially ordered set such that any
two elements in the subset are incomparable.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
topo_order: list or tuple, optional
A topological order for G (if None, the function will compute one)
Returns
-------
generator object
Raises
------
NetworkXNotImplemented
If `G` is not directed
NetworkXUnfeasible
If `G` contains a cycle
Notes
-----
This function was originally developed by Peter Jipsen and Franco Saliola
for the SAGE project. It's included in NetworkX with permission from the
authors. Original SAGE code at:
https://github.com/sagemath/sage/blob/master/src/sage/combinat/posets/hasse_diagram.py
References
----------
.. [1] Free Lattices, by R. Freese, J. Jezek and J. B. Nation,
AMS, Vol 42, 1995, p. 226.
"""
if topo_order is None:
topo_order = list(nx.topological_sort(G))
TC = nx.transitive_closure_dag(G, topo_order)
antichains_stacks = [([], list(reversed(topo_order)))]
while antichains_stacks:
(antichain, stack) = antichains_stacks.pop()
# Invariant:
# - the elements of antichain are independent
# - the elements of stack are independent from those of antichain
yield antichain
while stack:
x = stack.pop()
new_antichain = antichain + [x]
new_stack = [t for t in stack if not ((t in TC[x]) or (x in TC[t]))]
antichains_stacks.append((new_antichain, new_stack))
[docs]@not_implemented_for("undirected")
def dag_longest_path(G, weight="weight", default_weight=1, topo_order=None):
"""Returns the longest path in a directed acyclic graph (DAG).
If `G` has edges with `weight` attribute the edge data are used as
weight values.
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
weight : str, optional
Edge data key to use for weight
default_weight : int, optional
The weight of edges that do not have a weight attribute
topo_order: list or tuple, optional
A topological order for G (if None, the function will compute one)
Returns
-------
list
Longest path
Raises
------
NetworkXNotImplemented
If `G` is not directed
See also
--------
dag_longest_path_length
"""
if not G:
return []
if topo_order is None:
topo_order = nx.topological_sort(G)
dist = {} # stores {v : (length, u)}
for v in topo_order:
us = [
(dist[u][0] + data.get(weight, default_weight), u)
for u, data in G.pred[v].items()
]
# Use the best predecessor if there is one and its distance is
# non-negative, otherwise terminate.
maxu = max(us, key=lambda x: x[0]) if us else (0, v)
dist[v] = maxu if maxu[0] >= 0 else (0, v)
u = None
v = max(dist, key=lambda x: dist[x][0])
path = []
while u != v:
path.append(v)
u = v
v = dist[v][1]
path.reverse()
return path
[docs]@not_implemented_for("undirected")
def dag_longest_path_length(G, weight="weight", default_weight=1):
"""Returns the longest path length in a DAG
Parameters
----------
G : NetworkX DiGraph
A directed acyclic graph (DAG)
weight : string, optional
Edge data key to use for weight
default_weight : int, optional
The weight of edges that do not have a weight attribute
Returns
-------
int
Longest path length
Raises
------
NetworkXNotImplemented
If `G` is not directed
See also
--------
dag_longest_path
"""
path = nx.dag_longest_path(G, weight, default_weight)
path_length = 0
for (u, v) in pairwise(path):
path_length += G[u][v].get(weight, default_weight)
return path_length
def root_to_leaf_paths(G):
"""Yields root-to-leaf paths in a directed acyclic graph.
`G` must be a directed acyclic graph. If not, the behavior of this
function is undefined. A "root" in this graph is a node of in-degree
zero and a "leaf" a node of out-degree zero.
When invoked, this function iterates over each path from any root to
any leaf. A path is a list of nodes.
"""
roots = (v for v, d in G.in_degree() if d == 0)
leaves = (v for v, d in G.out_degree() if d == 0)
all_paths = partial(nx.all_simple_paths, G)
# TODO In Python 3, this would be better as `yield from ...`.
return chaini(starmap(all_paths, product(roots, leaves)))
[docs]@not_implemented_for("multigraph")
@not_implemented_for("undirected")
def dag_to_branching(G):
"""Returns a branching representing all (overlapping) paths from
root nodes to leaf nodes in the given directed acyclic graph.
As described in :mod:`networkx.algorithms.tree.recognition`, a
*branching* is a directed forest in which each node has at most one
parent. In other words, a branching is a disjoint union of
*arborescences*. For this function, each node of in-degree zero in
`G` becomes a root of one of the arborescences, and there will be
one leaf node for each distinct path from that root to a leaf node
in `G`.
Each node `v` in `G` with *k* parents becomes *k* distinct nodes in
the returned branching, one for each parent, and the sub-DAG rooted
at `v` is duplicated for each copy. The algorithm then recurses on
the children of each copy of `v`.
Parameters
----------
G : NetworkX graph
A directed acyclic graph.
Returns
-------
DiGraph
The branching in which there is a bijection between root-to-leaf
paths in `G` (in which multiple paths may share the same leaf)
and root-to-leaf paths in the branching (in which there is a
unique path from a root to a leaf).
Each node has an attribute 'source' whose value is the original
node to which this node corresponds. No other graph, node, or
edge attributes are copied into this new graph.
Raises
------
NetworkXNotImplemented
If `G` is not directed, or if `G` is a multigraph.
HasACycle
If `G` is not acyclic.
Examples
--------
To examine which nodes in the returned branching were produced by
which original node in the directed acyclic graph, we can collect
the mapping from source node to new nodes into a dictionary. For
example, consider the directed diamond graph::
>>> from collections import defaultdict
>>> from operator import itemgetter
>>>
>>> G = nx.DiGraph(nx.utils.pairwise("abd"))
>>> G.add_edges_from(nx.utils.pairwise("acd"))
>>> B = nx.dag_to_branching(G)
>>>
>>> sources = defaultdict(set)
>>> for v, source in B.nodes(data="source"):
... sources[source].add(v)
>>> len(sources["a"])
1
>>> len(sources["d"])
2
To copy node attributes from the original graph to the new graph,
you can use a dictionary like the one constructed in the above
example::
>>> for source, nodes in sources.items():
... for v in nodes:
... B.nodes[v].update(G.nodes[source])
Notes
-----
This function is not idempotent in the sense that the node labels in
the returned branching may be uniquely generated each time the
function is invoked. In fact, the node labels may not be integers;
in order to relabel the nodes to be more readable, you can use the
:func:`networkx.convert_node_labels_to_integers` function.
The current implementation of this function uses
:func:`networkx.prefix_tree`, so it is subject to the limitations of
that function.
"""
if has_cycle(G):
msg = "dag_to_branching is only defined for acyclic graphs"
raise nx.HasACycle(msg)
paths = root_to_leaf_paths(G)
B = nx.prefix_tree(paths)
# Remove the synthetic `root`(0) and `NIL`(-1) nodes from the tree
B.remove_node(0)
B.remove_node(-1)
return B
```