"""Node statistics.
This module is part of the stats package, and it defines node-level statistics. That
is, each function defined in this module is assumed to define a node-quantity mapping.
Each callable defined here is accessible via a `Network` object, or a
:class:`~xgi.core.views.NodeView` object. For more details, see the `tutorial
<https://xgi.readthedocs.io/en/stable/api/tutorials/focus_6.html>`_.
Examples
--------
>>> import xgi
>>> H = xgi.DiHypergraph([[{1, 2}, {5, 6}], [{4}, {1, 3}]])
>>> H.degree()
{1: 2, 2: 1, 5: 1, 6: 1, 4: 1, 3: 1}
>>> H.nodes.degree.asdict()
{1: 2, 2: 1, 5: 1, 6: 1, 4: 1, 3: 1}
"""
__all__ = [
"attrs",
"degree",
"in_degree",
"out_degree",
]
[docs]def attrs(net, bunch, attr=None, missing=None):
"""Access node attributes.
Parameters
----------
net : xgi.Hypergraph
The network.
bunch : Iterable
Nodes in `net`.
attr : str | None (default)
If None, return all attributes. Otherwise, return a single attribute with name
`attr`.
missing : Any
Value to impute in case a node does not have an attribute with name `attr`.
Default is None.
Returns
-------
dict
If attr is None, return a nested dict of the form `{node: {"attr": val}}`.
Otherwise, return a simple dict of the form `{node: val}`.
Notes
-----
When requesting all attributes (i.e. when `attr` is None), no value is imputed.
Examples
--------
>>> import xgi
>>> H = xgi.DiHypergraph()
>>> H.add_nodes_from([
... (1, {"color": "red", "name": "horse"}),
... (2, {"color": "blue", "name": "pony"}),
... (3, {"color": "yellow", "name": "zebra"}),
... (4, {"color": "red", "name": "orangutan", "age": 20}),
... (5, {"color": "blue", "name": "fish", "age": 2}),
... ])
Access all attributes as different types.
>>> H.nodes.attrs.asdict() # doctest: +NORMALIZE_WHITESPACE
{1: {'color': 'red', 'name': 'horse'},
2: {'color': 'blue', 'name': 'pony'},
3: {'color': 'yellow', 'name': 'zebra'},
4: {'color': 'red', 'name': 'orangutan', 'age': 20},
5: {'color': 'blue', 'name': 'fish', 'age': 2}}
>>> H.nodes.attrs.asnumpy() # doctest: +NORMALIZE_WHITESPACE
array([{'color': 'red', 'name': 'horse'},
{'color': 'blue', 'name': 'pony'},
{'color': 'yellow', 'name': 'zebra'},
{'color': 'red', 'name': 'orangutan', 'age': 20},
{'color': 'blue', 'name': 'fish', 'age': 2}],
dtype=object)
Access a single attribute as different types.
>>> H.nodes.attrs('color').asdict()
{1: 'red', 2: 'blue', 3: 'yellow', 4: 'red', 5: 'blue'}
>>> H.nodes.attrs('color').aslist()
['red', 'blue', 'yellow', 'red', 'blue']
By default, None is imputed when a node does not have the requested attribute.
>>> H.nodes.attrs('age').asdict()
{1: None, 2: None, 3: None, 4: 20, 5: 2}
Use `missing` to change the imputed value.
>>> H.nodes.attrs('age', missing=100).asdict()
{1: 100, 2: 100, 3: 100, 4: 20, 5: 2}
"""
if isinstance(attr, str):
return {n: net._node_attr[n].get(attr, missing) for n in bunch}
elif attr is None:
return {n: net._node_attr[n] for n in bunch}
else:
raise ValueError('"attr" must be str or None')
[docs]def degree(net, bunch, order=None, weight=None):
"""Node degree.
The degree of a node is the number of edges it belongs to,
regardless of whether it is in the head or the tail.
Parameters
----------
net : xgi.Hypergraph
The network.
bunch : Iterable
Nodes in `net`.
order : int | None
If not None (default), only count the edges of the given order.
weight : str | None
If not None, specifies the name of the edge attribute that determines the weight
of each edge.
Returns
-------
dict
Examples
--------
>>> import xgi
>>> H = xgi.DiHypergraph([[{1, 2}, {5, 6}], [{4}, {1, 3}]])
>>> H.nodes.degree.asdict()
{1: 2, 2: 1, 5: 1, 6: 1, 4: 1, 3: 1}
"""
if order is None and weight is None:
return {n: len(net._node[n]["in"].union(net._node[n]["out"])) for n in bunch}
if order is None and weight:
return {
n: sum(
net._edge_attr[e].get(weight, 1)
for e in net._node[n]["in"].union(net._node[n]["out"])
)
for n in bunch
}
if order is not None and weight is None:
return {
n: sum(
1
for e in net._node[n]["in"].union(net._node[n]["out"])
if len(net._edge[e]["in"].union(net._edge[e]["out"])) == order + 1
)
for n in bunch
}
if order is not None and weight:
return {
n: sum(
net._edge_attr[e].get(weight, 1)
for e in net._node[n]["in"].union(net._node[n]["out"])
if len(net._edge[e]["in"].union(net._edge[e]["out"])) == order + 1
)
for n in bunch
}
[docs]def in_degree(net, bunch, order=None, weight=None):
"""Node in-degree.
The in-degree of a node is the number of edges for which
the node is in the head (it is a receiver).
Parameters
----------
net : xgi.Hypergraph
The network.
bunch : Iterable
Nodes in `net`.
order : int | None
If not None (default), only count the edges of the given order.
weight : str | None
If not None, specifies the name of the edge attribute that determines the weight
of each edge.
Returns
-------
dict
Examples
--------
>>> import xgi
>>> H = xgi.DiHypergraph([[{1, 2}, {5, 6}], [{4}, {1, 3}]])
>>> H.nodes.in_degree.asdict()
{1: 1, 2: 0, 5: 1, 6: 1, 4: 0, 3: 1}
"""
if order is None and weight is None:
return {n: len(net._node[n]["in"]) for n in bunch}
if order is None and weight:
return {
n: sum(net._edge_attr[e].get(weight, 1) for e in net._node[n]["in"])
for n in bunch
}
if order is not None and weight is None:
return {
n: sum(
1
for e in net._node[n]["in"]
if len(net._edge[e]["in"].union(net._edge[e]["out"])) == order + 1
)
for n in bunch
}
if order is not None and weight:
return {
n: sum(
net._edge_attr[e].get(weight, 1)
for e in net._node[n]["in"]
if len(net._edge[e]["in"].union(net._edge[e]["out"])) == order + 1
)
for n in bunch
}
[docs]def out_degree(net, bunch, order=None, weight=None):
"""Node out-degree.
The out-degree of a node is the number of edges for which
the node is in the tail (it is a sender).
Parameters
----------
net : xgi.Hypergraph
The network.
bunch : Iterable
Nodes in `net`.
order : int | None
If not None (default), only count the edges of the given order.
weight : str | None
If not None, specifies the name of the edge attribute that determines the weight
of each edge.
Returns
-------
dict
Examples
--------
>>> import xgi
>>> H = xgi.DiHypergraph([[{1, 2}, {5, 6}], [{4}, {1, 3}]])
>>> H.nodes.out_degree.asdict()
{1: 1, 2: 1, 5: 0, 6: 0, 4: 1, 3: 0}
"""
if order is None and weight is None:
return {n: len(net._node[n]["out"]) for n in bunch}
if order is None and weight:
return {
n: sum(net._edge_attr[e].get(weight, 1) for e in net._node[n]["out"])
for n in bunch
}
if order is not None and weight is None:
return {
n: sum(
1
for e in net._node[n]["out"]
if len(net._edge[e]["in"].union(net._edge[e]["out"])) == order + 1
)
for n in bunch
}
if order is not None and weight:
return {
n: sum(
net._edge_attr[e].get(weight, 1)
for e in net._node[n]["out"]
if len(net._edge[e]["in"].union(net._edge[e]["out"])) == order + 1
)
for n in bunch
}
