"""Module for graph traversal methods."""
from __future__ import absolute_import
from ._ffi.function import _init_api
from . import backend as F
from . import utils
__all__ = ['bfs_nodes_generator', 'bfs_edges_generator',
'topological_nodes_generator',
'dfs_edges_generator', 'dfs_labeled_edges_generator',]
[docs]def bfs_nodes_generator(graph, source, reverse=False):
"""Node frontiers generator using breadth-first search.
Parameters
----------
graph : DGLGraph
The graph object.
source : list, tensor of nodes
Source nodes.
reverse : bool, default False
If True, traverse following the in-edge direction.
Returns
-------
list of node frontiers
Each node frontier is a list or tensor of node ids.
Examples
--------
Given a graph (directed, edges from small node id to large):
::
2 - 4
/ \\
0 - 1 - 3 - 5
>>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
>>> list(dgl.bfs_nodes_generator(g, 0))
[tensor([0]), tensor([1]), tensor([2, 3]), tensor([4, 5])]
"""
ghandle = graph._graph._handle
source = utils.toindex(source)
ret = _CAPI_DGLBFSNodes(ghandle, source.todgltensor(), reverse)
all_nodes = utils.toindex(ret(0)).tousertensor()
# TODO(minjie): how to support directly creating python list
sections = utils.toindex(ret(1)).tonumpy().tolist()
node_frontiers = F.split(all_nodes, sections, dim=0)
return node_frontiers
[docs]def bfs_edges_generator(graph, source, reverse=False):
"""Edges frontiers generator using breadth-first search.
Parameters
----------
graph : DGLGraph
The graph object.
source : list, tensor of nodes
Source nodes.
reverse : bool, default False
If True, traverse following the in-edge direction.
Returns
-------
list of edge frontiers
Each edge frontier is a list or tensor of edge ids.
Examples
--------
Given a graph (directed, edges from small node id to large, sorted
in lexicographical order of source-destination node id tuple):
::
2 - 4
/ \\
0 - 1 - 3 - 5
>>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
>>> list(dgl.bfs_edges_generator(g, 0))
[tensor([0]), tensor([1, 2]), tensor([4, 5])]
"""
ghandle = graph._graph._handle
source = utils.toindex(source)
ret = _CAPI_DGLBFSEdges(ghandle, source.todgltensor(), reverse)
all_edges = utils.toindex(ret(0)).tousertensor()
# TODO(minjie): how to support directly creating python list
sections = utils.toindex(ret(1)).tonumpy().tolist()
edge_frontiers = F.split(all_edges, sections, dim=0)
return edge_frontiers
[docs]def topological_nodes_generator(graph, reverse=False):
"""Node frontiers generator using topological traversal.
Parameters
----------
graph : DGLGraph
The graph object.
reverse : bool, optional
If True, traverse following the in-edge direction.
Returns
-------
list of node frontiers
Each node frontier is a list or tensor of node ids.
Examples
--------
Given a graph (directed, edges from small node id to large):
::
2 - 4
/ \\
0 - 1 - 3 - 5
>>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
>>> list(dgl.topological_nodes_generator(g))
[tensor([0]), tensor([1]), tensor([2]), tensor([3, 4]), tensor([5])]
"""
ghandle = graph._graph._handle
ret = _CAPI_DGLTopologicalNodes(ghandle, reverse)
all_nodes = utils.toindex(ret(0)).tousertensor()
# TODO(minjie): how to support directly creating python list
sections = utils.toindex(ret(1)).tonumpy().tolist()
return F.split(all_nodes, sections, dim=0)
[docs]def dfs_edges_generator(graph, source, reverse=False):
"""Edge frontiers generator using depth-first-search (DFS).
Multiple source nodes can be specified to start the DFS traversal. One
needs to make sure that each source node belongs to different connected
component, so the frontiers can be easily merged. Otherwise, the behavior
is undefined.
Parameters
----------
graph : DGLGraph
The graph object.
source : list, tensor of nodes
Source nodes.
reverse : bool, optional
If True, traverse following the in-edge direction.
Returns
-------
list of edge frontiers
Each edge frontier is a list or tensor of edge ids.
Examples
--------
Given a graph (directed, edges from small node id to large):
::
2 - 4
/ \\
0 - 1 - 3 - 5
Edge addition order [(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)]
>>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
>>> list(dgl.dfs_edges_generator(g, 0))
[tensor([0]), tensor([1]), tensor([3]), tensor([5]), tensor([4])]
"""
ghandle = graph._graph._handle
source = utils.toindex(source)
ret = _CAPI_DGLDFSEdges(ghandle, source.todgltensor(), reverse)
all_edges = utils.toindex(ret(0)).tousertensor()
# TODO(minjie): how to support directly creating python list
sections = utils.toindex(ret(1)).tonumpy().tolist()
return F.split(all_edges, sections, dim=0)
[docs]def dfs_labeled_edges_generator(
graph,
source,
reverse=False,
has_reverse_edge=False,
has_nontree_edge=False,
return_labels=True):
"""Produce edges in a depth-first-search (DFS) labeled by type.
There are three labels: FORWARD(0), REVERSE(1), NONTREE(2)
A FORWARD edge is one in which `u` has been visited but `v` has not. A
REVERSE edge is one in which both `u` and `v` have been visited and the
edge is in the DFS tree. A NONTREE edge is one in which both `u` and `v`
have been visited but the edge is NOT in the DFS tree.
See ``networkx``'s :func:`dfs_labeled_edges
<networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges>`
for more details.
Multiple source nodes can be specified to start the DFS traversal. One
needs to make sure that each source node belongs to different connected
component, so the frontiers can be easily merged. Otherwise, the behavior
is undefined.
Parameters
----------
graph : DGLGraph
The graph object.
source : list, tensor of nodes
Source nodes.
reverse : bool, optional
If true, traverse following the in-edge direction.
has_reverse_edge : bool, optional
True to include reverse edges.
has_nontree_edge : bool, optional
True to include nontree edges.
return_labels : bool, optional
True to return the labels of each edge.
Returns
-------
list of edge frontiers
Each edge frontier is a list or tensor of edge ids.
list of list of int
Label of each edge, organized in the same order as the edge frontiers.
Examples
--------
Given a graph (directed, edges from small node id to large):
::
2 - 4
/ \\
0 - 1 - 3 - 5
Edge addition order [(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)]
>>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
>>> list(dgl.dfs_labeled_edges_generator(g, 0, has_nontree_edge=True))
(tensor([0]), tensor([1]), tensor([3]), tensor([5]), tensor([4]), tensor([2])),
(tensor([0]), tensor([0]), tensor([0]), tensor([0]), tensor([0]), tensor([2]))
"""
ghandle = graph._graph._handle
source = utils.toindex(source)
ret = _CAPI_DGLDFSLabeledEdges(
ghandle,
source.todgltensor(),
reverse,
has_reverse_edge,
has_nontree_edge,
return_labels)
all_edges = utils.toindex(ret(0)).tousertensor()
# TODO(minjie): how to support directly creating python list
if return_labels:
all_labels = utils.toindex(ret(1)).tousertensor()
sections = utils.toindex(ret(2)).tonumpy().tolist()
return (F.split(all_edges, sections, dim=0),
F.split(all_labels, sections, dim=0))
else:
sections = utils.toindex(ret(1)).tonumpy().tolist()
return F.split(all_edges, sections, dim=0)
_init_api("dgl.traversal")