xgi.stats.edgestats#

Edge statistics.

This module is part of the stats package, and it defines edge-level statistics. That is, each function defined in this module is assumed to define an edge-quantity mapping. Each callable defined here is accessible via a Network object, or a EdgeView object. For more details, see the tutorial.

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> H.order()
{0: 2, 1: 3, 2: 2}
>>> H.edges.order.asdict()
{0: 2, 1: 3, 2: 2}

Functions

attrs(net, bunch, attr=None, missing=None)[source]#

Access edge 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 an edge does not have an attribute with name attr. Default is None.

Returns:

If attr is None, return a nested dict of the form {edge: {“attr”: val}}. Otherwise, return a simple dict of the form {edge: val}.

Return type:

dict

Notes

When requesting all attributes (i.e. when attr is None), no value is imputed.

Examples

>>> import xgi
>>> H = xgi.Hypergraph()
>>> edges = [
...     ([0, 1], 'one', {'color': 'red'}),
...     ([1, 2], 'two', {'color': 'black', 'age': 30}),
...     ([2, 3, 4], 'three', {'color': 'blue', 'age': 40}),
... ]
>>> H.add_edges_from(edges)

Access all attributes as different types.

>>> H.edges.attrs.asdict() 
{'one': {'color': 'red'},
 'two': {'color': 'black', 'age': 30},
 'three': {'color': 'blue', 'age': 40}}
>>> H.edges.attrs.asnumpy() 
array([{'color': 'red'},
       {'color': 'black', 'age': 30},
       {'color': 'blue', 'age': 40}],
       dtype=object)

Access a single attribute as different types.

>>> H.edges.attrs('color').asdict()
{'one': 'red', 'two': 'black', 'three': 'blue'}
>>> H.edges.attrs('color').aslist()
['red', 'black', 'blue']

By default, None is imputed when a node does not have the requested attribute.

>>> H.edges.attrs('age').asdict()
{'one': None, 'two': 30, 'three': 40}

Use missing to change the imputed value.

>>> H.edges.attrs('age', missing=100).asdict()
{'one': 100, 'two': 30, 'three': 40}
order(net, bunch, degree=None)[source]#

Edge order.

The order of an edge is the number of nodes it contains minus 1.

Parameters:

  • net (xgi.Hypergraph) – The network.

  • bunch (Iterable) – Edges in net.

  • degree (int | None) – If not None (default), count only those member nodes with the specified degree.

Return type:

dict

See also

size

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> H.edges.order.asdict()
{0: 2, 1: 3, 2: 2}
>>> H.edges.order(degree=2).asdict()
{0: 0, 1: 2, 2: 1}
size(net, bunch, degree=None)[source]#

Edge size.

The size of an edge is the number of nodes it contains.

Parameters:

  • net (xgi.Hypergraph) – The network.

  • bunch (Iterable) – Edges in net.

Return type:

dict

See also

order

Examples

>>> import xgi
>>> H = xgi.Hypergraph([[1, 2, 3], [2, 3, 4, 5], [3, 4, 5]])
>>> H.edges.size.asdict()
{0: 3, 1: 4, 2: 3}
node_edge_centrality(net, bunch, f=<function <lambda>>, g=<function <lambda>>, phi=<function <lambda>>, psi=<function <lambda>>, max_iter=100, tol=1e-06)[source]#

Edge component of the nonlinear node-edge centrality.

See xgi.algorithms.centrality.node_edge_centrality() for the definition, parameters, and references.

Parameters:

  • net (Hypergraph) – The hypergraph of interest.

  • bunch (Iterable) – Edges in net.

Returns:

Edge centralities.

Return type:

dict

See also

node_edge_centrality