yawning_titan.networks.network.Network#

class yawning_titan.networks.network.Network(set_random_entry_nodes=False, random_entry_node_preference=RandomEntryNodePreference.NONE, num_of_random_entry_nodes=0, set_random_high_value_nodes=False, random_high_value_node_preference=RandomHighValueNodePreference.NONE, num_of_random_high_value_nodes=0, set_random_vulnerabilities=False, node_vulnerability_lower_bound=0.01, node_vulnerability_upper_bound=1, doc_metadata=None, **kwargs)[source]#

Bases: Graph

A Network that the NetworkInterface interacts with.

Network extends networkx.Graph.

Example

The Network constructor.

Parameters:

set_random_entry_nodes – Whether entry nodes are set at random if not set in nodes. Default value of False.
random_entry_node_preference – The random entry node placement preference as an instance of yawning_titan.networks.network.RandomEntryNodePreference. Default value of RandomEntryNodePreference.NONE.
num_of_random_entry_nodes – The number of random entry nodes that will be attempted to be set.
set_random_high_value_nodes – Whether high value nodes are set at random if not set in nodes. Default value of False.
random_high_value_node_preference – The random high value node placement preference as an instance of yawning_titan.networks.network.RandomHighValueNodePreference. Default value of RandomHighValueNodePreference.NONE.
num_of_random_high_value_nodes – The number of random high value nodes that will be attempted to be set.
set_random_vulnerabilities – Whether node vulnerability scores are set at random.
node_vulnerability_lower_bound – The lower-bound of a nodes vulnerability score. Default value of 0.01.
node_vulnerability_upper_bound – The upper-bound of a nodes vulnerability score. Default value of 0.01.

Methods

`add_edge`	Add an edge between 2 nodes in the network.
`add_edges_from`	Add all the edges in ebunch_to_add.
`add_edges_from_dict`	Add edges to the graph with properties defined from a dictionary.
`add_node`	Add a node to the network.
`add_nodes_from`	Add multiple nodes.
`add_nodes_from_dict`	Add nodes to the graph with properties defined from a dictionary.
`add_weighted_edges_from`	Add weighted edges in ebunch_to_add with specified weight attr
`adjacency`	Returns an iterator over (node, adjacency dict) tuples for all nodes.
`clear`	Remove all nodes and edges from the graph.
`clear_edges`	Remove all edges from the graph without altering nodes.
`clear_special_nodes`	Remove all special node designations.
`copy`	Returns a copy of the graph.
`create`	Create an instance on :class: Network from a dictionary.
`edge_subgraph`	Returns the subgraph induced by the specified edges.
`get_edge_data`	Returns the attribute dictionary associated with edge (u, v).
`get_node_from_name`	Return the first node that has a given name.
`get_node_from_uuid`	Return the first node that has a given uuid.
`get_nodes`	Get all of the nodes from the network and apply a filter(s) to extract a specific subset of the nodes.
`has_edge`	Returns True if the edge (u, v) is in the graph.
`has_node`	Returns True if the graph contains the node n.
`is_directed`	Returns True if graph is directed, False otherwise.
`is_multigraph`	Returns True if graph is a multigraph, False otherwise.
`nbunch_iter`	Returns an iterator over nodes contained in nbunch that are also in the graph.
`neighbors`	Returns an iterator over all neighbors of node n.
`number_of_edges`	Returns the number of edges between two nodes.
`number_of_nodes`	Returns the number of nodes in the graph.
`order`	Returns the number of nodes in the graph.
`remove_edge`	Remove an edge between 2 nodes in the network.
`remove_edges_from`	Remove all edges specified in ebunch.
`remove_node`	Remove a node from the network.
`remove_nodes_from`	Remove multiple nodes.
`reset`	Resets the network.
`reset_random_entry_nodes`	Set the entry nodes.
`reset_random_high_value_nodes`	Sets up the high value nodes (HVNs) to be used by the training environment.
`reset_random_vulnerabilities`	Regenerate random vulnerabilities for every node in the network.
`set_from_dict`	Set the values of existing network attributes from those contained in a dictionary.
`set_node_positions`	Sets the Node positions of the current Network.
`show`	Show details of all Nodes in the Network.
`size`	Returns the number of edges or total of all edge weights.
`subgraph`	Returns a SubGraph view of the subgraph induced on nodes.
`to_adj_matrix_and_positions`	Represent the network by its adjacency matrix and a dictionary of node names to positions.
`to_dict`	Represent the Network as a dictionary.
`to_directed`	Returns a directed representation of the graph.
`to_directed_class`	Returns the class to use for empty directed copies.
`to_undirected`	Returns an undirected copy of the graph.
`to_undirected_class`	Returns the class to use for empty undirected copies.
`update`	Update the graph using nodes/edges/graphs as input.

Attributes

`adj`	Graph adjacency object holding the neighbors of each node.
`deceptive_nodes`	A list of the deceptive nodes in the network.
`degree`	A DegreeView for the Graph as G.degree or G.degree().
`doc_metadata`	The configs document metadata.
`edges`	An EdgeView of the Graph as G.edges or G.edges().
`entry_nodes`	A list of the entry nodes in the network.
`high_value_nodes`	A list of the high value nodes in the network.
`name`	String identifier of the graph.
`node_vulnerability_lower_bound`	A lower vulnerability means that a node is less likely to be compromised.
`nodes`	Access the nodes property from the superclass which has list properties but is a NodeView instance
`num_possible_high_value_nodes`	Maximum number of allowed high value nodes in the network.
`set_random_entry_nodes`	If no entry nodes are added, set them at random.
`random_entry_node_preference`	The type of random entry node preference.
`num_of_random_entry_nodes`	The number of random entry nodes to be generated.
`set_random_high_value_nodes`	If no high value nodes are added, set them at random.
`random_high_value_node_preference`	The type of random high value node preference.
`num_of_random_high_value_nodes`	The number of random high_value nodes to be generated.
`set_random_vulnerabilities`	If True, random vulnerability is set for each node using the upper and lower bounds.
`node_vulnerability_upper_bound`	A higher vulnerability means that a node is more vulnerable.

set_random_entry_nodes#: If no entry nodes are added, set them at random. Default is False.

random_entry_node_preference#: The type of random entry node preference.

num_of_random_entry_nodes#: The number of random entry nodes to be generated.

set_random_high_value_nodes#: If no high value nodes are added, set them at random. Default is False.

random_high_value_node_preference#: The type of random high value node preference.

num_of_random_high_value_nodes#: The number of random high_value nodes to be generated.

set_random_vulnerabilities#: If True, random vulnerability is set for each node using the upper and lower bounds.

node_vulnerability_upper_bound#: A higher vulnerability means that a node is more vulnerable. Default value is 1.

property nodes#: Access the nodes property from the superclass which has list properties but is a NodeView instance

property high_value_nodes#: A list of the high value nodes in the network.

property entry_nodes#: A list of the entry nodes in the network.

property deceptive_nodes#: A list of the deceptive nodes in the network.

property num_possible_high_value_nodes#

Maximum number of allowed high value nodes in the network.

Number of possible high value nodes calculated by seeing how many nodes there are minus the entry nodes, then only having 15% of the nodes left over to be high value nodes.

property node_vulnerability_lower_bound#: A lower vulnerability means that a node is less likely to be compromised. Default value is 0.

property doc_metadata#: The configs document metadata.

add_node(node_for_adding, **kwargs)[source]#

Add a node to the network.

Extend the add_node method of the superclass.

if the node_for_adding is a special node then check that there are no intersections between hvn and entry_node’s.

remove_node(n)[source]#

Remove a node from the network.

Extend the remove_node method of the superclass.

add_edge(u_of_edge, v_of_edge, **kwargs)[source]#

Add an edge between 2 nodes in the network.

Extend the add_edge method of the superclass.

remove_edge(u, v)[source]#

Remove an edge between 2 nodes in the network.

Extend the remove_edge method of the superclass.

reset()[source]#

Resets the network.

This is done by calling:

reset_random_entry_nodes()
reset_random_high_value_nodes
reset_random_vulnerabilities()

show(verbose=False)[source]#

Show details of all Nodes in the Network.

Parameters:: verbose – If True, all Node attributes are shown, otherwise just the uuid is shown.

get_nodes(filter_true_compromised=False, filter_blue_view_compromised=False, filter_true_safe=False, filter_blue_view_safe=False, filter_isolated=False, filter_non_isolated=False, filter_deceptive=False, filter_non_deceptive=False, key_by_name=False, as_list=False)[source]#

Get all of the nodes from the network and apply a filter(s) to extract a specific subset of the nodes.

Parameters:

filter_true_compromised – Filter so only nodes that are compromised remain
filter_blue_view_compromised – Filter so only nodes that blue can see are compromised remain
filter_true_safe – Filter so only nodes that are safe remain
filter_blue_view_safe – Filter so only nodes that blue can see are safe remain
filter_isolated – Filter so only isolated nodes remain
filter_non_isolated – Filter so only connected nodes remain
filter_deceptive – Filter so only deceptive nodes remain
filter_non_deceptive – Filter so only non-deceptive nodes remain

Returns:

A list of nodes

add_edges_from(ebunch_to_add, **attr)#

Add all the edges in ebunch_to_add.

Parameters:

ebunch_to_add (container of edges) – Each edge given in the container will be added to the graph. The edges must be given as as 2-tuples (u, v) or 3-tuples (u, v, d) where d is a dictionary containing edge data.
attr (keyword arguments, optional) – Edge data (or labels or objects) can be assigned using keyword arguments.

See also

add_edge: add a single edge
add_weighted_edges_from: convenient way to add weighted edges

Notes

Adding the same edge twice has no effect but any edge data will be updated when each duplicate edge is added.

Edge attributes specified in an ebunch take precedence over attributes specified via keyword arguments.

Examples

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
>>> e = zip(range(0, 3), range(1, 4))
>>> G.add_edges_from(e)  # Add the path graph 0-1-2-3

Associate data to edges

>>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
>>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")

add_nodes_from(nodes_for_adding, **attr)#

Add multiple nodes.

Parameters:

nodes_for_adding (iterable container) – A container of nodes (list, dict, set, etc.). OR A container of (node, attribute dict) tuples. Node attributes are updated using the attribute dict.
attr (keyword arguments, optional (default= no attributes)) – Update attributes for all nodes in nodes. Node attributes specified in nodes as a tuple take precedence over attributes specified via keyword arguments.

See also

add_node

Examples

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_nodes_from("Hello")
>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
>>> G.add_nodes_from(K3)
>>> sorted(G.nodes(), key=str)
[0, 1, 2, 'H', 'e', 'l', 'o']

Use keywords to update specific node attributes for every node.

>>> G.add_nodes_from([1, 2], size=10)
>>> G.add_nodes_from([3, 4], weight=0.4)

Use (node, attrdict) tuples to update attributes for specific nodes.

>>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
>>> G.nodes[1]["size"]
11
>>> H = nx.Graph()
>>> H.add_nodes_from(G.nodes(data=True))
>>> H.nodes[1]["size"]
11

add_weighted_edges_from(ebunch_to_add, weight='weight', **attr)#

Add weighted edges in ebunch_to_add with specified weight attr

Parameters:

ebunch_to_add (container of edges) – Each edge given in the list or container will be added to the graph. The edges must be given as 3-tuples (u, v, w) where w is a number.
weight (string, optional (default= 'weight')) – The attribute name for the edge weights to be added.
attr (keyword arguments, optional (default= no attributes)) – Edge attributes to add/update for all edges.

See also

add_edge: add a single edge
add_edges_from: add multiple edges

Notes

Adding the same edge twice for Graph/DiGraph simply updates the edge data. For MultiGraph/MultiDiGraph, duplicate edges are stored.

Examples

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_weighted_edges_from([(0, 1, 3.0), (1, 2, 7.5)])

property adj#

Graph adjacency object holding the neighbors of each node.

This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So G.adj[3][2][‘color’] = ‘blue’ sets the color of the edge (3, 2) to “blue”.

Iterating over G.adj behaves like a dict. Useful idioms include for nbr, datadict in G.adj[n].items():.

The neighbor information is also provided by subscripting the graph. So for nbr, foovalue in G[node].data(‘foo’, default=1): works.

For directed graphs, G.adj holds outgoing (successor) info.

adjacency()#

Returns an iterator over (node, adjacency dict) tuples for all nodes.

For directed graphs, only outgoing neighbors/adjacencies are included.

Returns:: adj_iter – An iterator over (node, adjacency dictionary) for all nodes in the graph.
Return type:: iterator

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> [(n, nbrdict) for n, nbrdict in G.adjacency()]
[(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]

adjlist_inner_dict_factory#: alias of dict

adjlist_outer_dict_factory#: alias of dict

clear()#

Remove all nodes and edges from the graph.

This also removes the name, and all graph, node, and edge attributes.

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.clear()
>>> list(G.nodes)
[]
>>> list(G.edges)
[]

clear_edges()#

Remove all edges from the graph without altering nodes.

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.clear_edges()
>>> list(G.nodes)
[0, 1, 2, 3]
>>> list(G.edges)
[]

copy(as_view=False)#

Returns a copy of the graph.

The copy method by default returns an independent shallow copy of the graph and attributes. That is, if an attribute is a container, that container is shared by the original an the copy. Use Python’s copy.deepcopy for new containers.

If as_view is True then a view is returned instead of a copy.

Notes

All copies reproduce the graph structure, but data attributes may be handled in different ways. There are four types of copies of a graph that people might want.

Deepcopy – A “deepcopy” copies the graph structure as well as all data attributes and any objects they might contain. The entire graph object is new so that changes in the copy do not affect the original object. (see Python’s copy.deepcopy)

Data Reference (Shallow) – For a shallow copy the graph structure is copied but the edge, node and graph attribute dicts are references to those in the original graph. This saves time and memory but could cause confusion if you change an attribute in one graph and it changes the attribute in the other. NetworkX does not provide this level of shallow copy.

Independent Shallow – This copy creates new independent attribute dicts and then does a shallow copy of the attributes. That is, any attributes that are containers are shared between the new graph and the original. This is exactly what dict.copy() provides. You can obtain this style copy using:

>>> G = nx.path_graph(5)
>>> H = G.copy()
>>> H = G.copy(as_view=False)
>>> H = nx.Graph(G)
>>> H = G.__class__(G)

Fresh Data – For fresh data, the graph structure is copied while new empty data attribute dicts are created. The resulting graph is independent of the original and it has no edge, node or graph attributes. Fresh copies are not enabled. Instead use:

>>> H = G.__class__()
>>> H.add_nodes_from(G)
>>> H.add_edges_from(G.edges)

View – Inspired by dict-views, graph-views act like read-only versions of the original graph, providing a copy of the original structure without requiring any memory for copying the information.

See the Python copy module for more information on shallow and deep copies, https://docs.python.org/3/library/copy.html.

Parameters:: as_view (bool, optional (default=False)) – If True, the returned graph-view provides a read-only view of the original graph without actually copying any data.
Returns:: G – A copy of the graph.
Return type:: Graph

See also

to_directed: return a directed copy of the graph.

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> H = G.copy()

property degree#

A DegreeView for the Graph as G.degree or G.degree().

The node degree is the number of edges adjacent to the node. The weighted node degree is the sum of the edge weights for edges incident to that node.

This object provides an iterator for (node, degree) as well as lookup for the degree for a single node.

Parameters:

nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
weight (string or None, optional (default=None)) – The name of an edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node.

Returns:

If a single node is requested
deg (int) – Degree of the node
OR if multiple nodes are requested
nd_view (A DegreeView object capable of iterating (node, degree) pairs)

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.degree[0]  # node 0 has degree 1
1
>>> list(G.degree([0, 1, 2]))
[(0, 1), (1, 2), (2, 2)]

edge_attr_dict_factory#: alias of dict

edge_subgraph(edges)#

Returns the subgraph induced by the specified edges.

The induced subgraph contains each edge in edges and each node incident to any one of those edges.

Parameters:: edges (iterable) – An iterable of edges in this graph.
Returns:: G – An edge-induced subgraph of this graph with the same edge attributes.
Return type:: Graph

Notes

The graph, edge, and node attributes in the returned subgraph view are references to the corresponding attributes in the original graph. The view is read-only.

To create a full graph version of the subgraph with its own copy of the edge or node attributes, use:

>>> G.edge_subgraph(edges).copy()  

Examples

>>> G = nx.path_graph(5)
>>> H = G.edge_subgraph([(0, 1), (3, 4)])
>>> list(H.nodes)
[0, 1, 3, 4]
>>> list(H.edges)
[(0, 1), (3, 4)]

property edges#

An EdgeView of the Graph as G.edges or G.edges().

edges(self, nbunch=None, data=False, default=None)

The EdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, G.edges[u, v][‘color’] provides the value of the color attribute for edge (u, v) while for (u, v, c) in G.edges.data(‘color’, default=’red’): iterates through all the edges yielding the color attribute with default ‘red’ if no color attribute exists.

Parameters:

nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
data (string or bool, optional (default=False)) – The edge attribute returned in 3-tuple (u, v, ddict[data]). If True, return edge attribute dict in 3-tuple (u, v, ddict). If False, return 2-tuple (u, v).
default (value, optional (default=None)) – Value used for edges that don’t have the requested attribute. Only relevant if data is not True or False.

Returns:

edges – A view of edge attributes, usually it iterates over (u, v) or (u, v, d) tuples of edges, but can also be used for attribute lookup as edges[u, v][‘foo’].

Return type:

EdgeView

Notes

Nodes in nbunch that are not in the graph will be (quietly) ignored. For directed graphs this returns the out-edges.

Examples

>>> G = nx.path_graph(3)  # or MultiGraph, etc
>>> G.add_edge(2, 3, weight=5)
>>> [e for e in G.edges]
[(0, 1), (1, 2), (2, 3)]
>>> G.edges.data()  # default data is {} (empty dict)
EdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
>>> G.edges.data("weight", default=1)
EdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
>>> G.edges([0, 3])  # only edges incident to these nodes
EdgeDataView([(0, 1), (3, 2)])
>>> G.edges(0)  # only edges incident to a single node (use G.adj[0]?)
EdgeDataView([(0, 1)])

get_edge_data(u, v, default=None)#

Returns the attribute dictionary associated with edge (u, v).

This is identical to G[u][v] except the default is returned instead of an exception if the edge doesn’t exist.

Parameters:

u (nodes) –
v (nodes) –
default (any Python object (default=None)) – Value to return if the edge (u, v) is not found.

Returns:

edge_dict – The edge attribute dictionary.

Return type:

dictionary

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G[0][1]
{}

Warning: Assigning to G[u][v] is not permitted. But it is safe to assign attributes G[u][v][‘foo’]

>>> G[0][1]["weight"] = 7
>>> G[0][1]["weight"]
7
>>> G[1][0]["weight"]
7

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.get_edge_data(0, 1)  # default edge data is {}
{}
>>> e = (0, 1)
>>> G.get_edge_data(*e)  # tuple form
{}
>>> G.get_edge_data("a", "b", default=0)  # edge not in graph, return 0
0

get_node_from_uuid(uuid)[source]#: Return the first node that has a given uuid.

graph_attr_dict_factory#: alias of dict

has_edge(u, v)#

Returns True if the edge (u, v) is in the graph.

This is the same as v in G[u] without KeyError exceptions.

Parameters:

u (nodes) – Nodes can be, for example, strings or numbers. Nodes must be hashable (and not None) Python objects.
v (nodes) – Nodes can be, for example, strings or numbers. Nodes must be hashable (and not None) Python objects.

Returns:

edge_ind – True if edge is in the graph, False otherwise.

Return type:

bool

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.has_edge(0, 1)  # using two nodes
True
>>> e = (0, 1)
>>> G.has_edge(*e)  #  e is a 2-tuple (u, v)
True
>>> e = (0, 1, {"weight": 7})
>>> G.has_edge(*e[:2])  # e is a 3-tuple (u, v, data_dictionary)
True

The following syntax are equivalent:

>>> G.has_edge(0, 1)
True
>>> 1 in G[0]  # though this gives KeyError if 0 not in G
True

has_node(n)#

Returns True if the graph contains the node n.

Identical to n in G

Parameters:: n (node) –

Examples

>>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.has_node(0)
True

It is more readable and simpler to use

>>> 0 in G
True

is_directed()#: Returns True if graph is directed, False otherwise.

is_multigraph()#: Returns True if graph is a multigraph, False otherwise.

property name#

String identifier of the graph.

This graph attribute appears in the attribute dict G.graph keyed by the string “name”. as well as an attribute (technically a property) G.name. This is entirely user controlled.

nbunch_iter(nbunch=None)#

Returns an iterator over nodes contained in nbunch that are also in the graph.

The nodes in nbunch are checked for membership in the graph and if not are silently ignored.

Parameters:: nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
Returns:: niter – An iterator over nodes in nbunch that are also in the graph. If nbunch is None, iterate over all nodes in the graph.
Return type:: iterator
Raises:: NetworkXError – If nbunch is not a node or or sequence of nodes. If a node in nbunch is not hashable.

See also

Graph.__iter__

Notes

When nbunch is an iterator, the returned iterator yields values directly from nbunch, becoming exhausted when nbunch is exhausted.

To test whether nbunch is a single node, one can use “if nbunch in self:”, even after processing with this routine.

If nbunch is not a node or a (possibly empty) sequence/iterator or None, a NetworkXError is raised. Also, if any object in nbunch is not hashable, a NetworkXError is raised.

neighbors(n)#

Returns an iterator over all neighbors of node n.

This is identical to iter(G[n])

Parameters:: n (node) – A node in the graph
Returns:: neighbors – An iterator over all neighbors of node n
Return type:: iterator
Raises:: NetworkXError – If the node n is not in the graph.

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> [n for n in G.neighbors(0)]
[1]

Notes

Alternate ways to access the neighbors are G.adj[n] or G[n]:

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edge("a", "b", weight=7)
>>> G["a"]
AtlasView({'b': {'weight': 7}})
>>> G = nx.path_graph(4)
>>> [n for n in G[0]]
[1]

node_attr_dict_factory#: alias of dict

node_dict_factory#: alias of dict

number_of_edges(u=None, v=None)#

Returns the number of edges between two nodes.

Parameters:

u (nodes, optional (default=all edges)) – If u and v are specified, return the number of edges between u and v. Otherwise return the total number of all edges.
v (nodes, optional (default=all edges)) – If u and v are specified, return the number of edges between u and v. Otherwise return the total number of all edges.

Returns:

nedges – The number of edges in the graph. If nodes u and v are specified return the number of edges between those nodes. If the graph is directed, this only returns the number of edges from u to v.

Return type:

int

See also

size

Examples

For undirected graphs, this method counts the total number of edges in the graph:

>>> G = nx.path_graph(4)
>>> G.number_of_edges()
3

If you specify two nodes, this counts the total number of edges joining the two nodes:

>>> G.number_of_edges(0, 1)
1

For directed graphs, this method can count the total number of directed edges from u to v:

>>> G = nx.DiGraph()
>>> G.add_edge(0, 1)
>>> G.add_edge(1, 0)
>>> G.number_of_edges(0, 1)
1

number_of_nodes()#

Returns the number of nodes in the graph.

Returns:: nnodes – The number of nodes in the graph.
Return type:: int

See also

order, __len__

Examples

>>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.number_of_nodes()
3

order()#

Returns the number of nodes in the graph.

Returns:: nnodes – The number of nodes in the graph.
Return type:: int

See also

number_of_nodes, __len__

Examples

>>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.order()
3

remove_edges_from(ebunch)#

Remove all edges specified in ebunch.

Parameters:

ebunch (list or container of edge tuples) –

Each edge given in the list or container will be removed from the graph. The edges can be:

2-tuples (u, v) edge between u and v.

3-tuples (u, v, k) where k is ignored.

See also

remove_edge: remove a single edge

Notes

Will fail silently if an edge in ebunch is not in the graph.

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> ebunch = [(1, 2), (2, 3)]
>>> G.remove_edges_from(ebunch)

remove_nodes_from(nodes)#

Remove multiple nodes.

Parameters:: nodes (iterable container) – A container of nodes (list, dict, set, etc.). If a node in the container is not in the graph it is silently ignored.

See also

remove_node

Examples

>>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> e = list(G.nodes)
>>> e
[0, 1, 2]
>>> G.remove_nodes_from(e)
>>> list(G.nodes)
[]

size(weight=None)#

Returns the number of edges or total of all edge weights.

Parameters:

weight (string or None, optional (default=None)) – The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1.

Returns:

size – The number of edges or (if weight keyword is provided) the total weight sum.

If weight is None, returns an int. Otherwise a float (or more general numeric if the weights are more general).

Return type:

numeric

See also

number_of_edges

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.size()
3

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edge("a", "b", weight=2)
>>> G.add_edge("b", "c", weight=4)
>>> G.size()
2
>>> G.size(weight="weight")
6.0

subgraph(nodes)#

Returns a SubGraph view of the subgraph induced on nodes.

The induced subgraph of the graph contains the nodes in nodes and the edges between those nodes.

Parameters:: nodes (list, iterable) – A container of nodes which will be iterated through once.
Returns:: G – A subgraph view of the graph. The graph structure cannot be changed but node/edge attributes can and are shared with the original graph.
Return type:: SubGraph View

Notes

The graph, edge and node attributes are shared with the original graph. Changes to the graph structure is ruled out by the view, but changes to attributes are reflected in the original graph.

To create a subgraph with its own copy of the edge/node attributes use: G.subgraph(nodes).copy()

For an inplace reduction of a graph to a subgraph you can remove nodes: G.remove_nodes_from([n for n in G if n not in set(nodes)])

Subgraph views are sometimes NOT what you want. In most cases where you want to do more than simply look at the induced edges, it makes more sense to just create the subgraph as its own graph with code like:

# Create a subgraph SG based on a (possibly multigraph) G
SG = G.__class__()
SG.add_nodes_from((n, G.nodes[n]) for n in largest_wcc)
if SG.is_multigraph():
    SG.add_edges_from((n, nbr, key, d)
        for n, nbrs in G.adj.items() if n in largest_wcc
        for nbr, keydict in nbrs.items() if nbr in largest_wcc
        for key, d in keydict.items())
else:
    SG.add_edges_from((n, nbr, d)
        for n, nbrs in G.adj.items() if n in largest_wcc
        for nbr, d in nbrs.items() if nbr in largest_wcc)
SG.graph.update(G.graph)

Examples

>>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> H = G.subgraph([0, 1, 2])
>>> list(H.edges)
[(0, 1), (1, 2)]

to_directed(as_view=False)#

Returns a directed representation of the graph.

Returns:: G – A directed graph with the same name, same nodes, and with each edge (u, v, data) replaced by two directed edges (u, v, data) and (v, u, data).
Return type:: DiGraph

Notes

This returns a “deepcopy” of the edge, node, and graph attributes which attempts to completely copy all of the data and references.

This is in contrast to the similar D=DiGraph(G) which returns a shallow copy of the data.

See the Python copy module for more information on shallow and deep copies, https://docs.python.org/3/library/copy.html.

Warning: If you have subclassed Graph to use dict-like objects in the data structure, those changes do not transfer to the DiGraph created by this method.

Examples

>>> G = nx.Graph()  # or MultiGraph, etc
>>> G.add_edge(0, 1)
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]

If already directed, return a (deep) copy

>>> G = nx.DiGraph()  # or MultiDiGraph, etc
>>> G.add_edge(0, 1)
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1)]

to_directed_class()#

Returns the class to use for empty directed copies.

If you subclass the base classes, use this to designate what directed class to use for to_directed() copies.

to_undirected(as_view=False)#

Returns an undirected copy of the graph.

Parameters:: as_view (bool (optional, default=False)) – If True return a view of the original undirected graph.
Returns:: G – A deepcopy of the graph.
Return type:: Graph/MultiGraph

Notes

This returns a “deepcopy” of the edge, node, and graph attributes which attempts to completely copy all of the data and references.

This is in contrast to the similar G = nx.DiGraph(D) which returns a shallow copy of the data.

See the Python copy module for more information on shallow and deep copies, https://docs.python.org/3/library/copy.html.

Warning: If you have subclassed DiGraph to use dict-like objects in the data structure, those changes do not transfer to the Graph created by this method.

Examples

>>> G = nx.path_graph(2)  # or MultiGraph, etc
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]
>>> G2 = H.to_undirected()
>>> list(G2.edges)
[(0, 1)]

to_undirected_class()#

Returns the class to use for empty undirected copies.

If you subclass the base classes, use this to designate what directed class to use for to_directed() copies.

update(edges=None, nodes=None)#

Update the graph using nodes/edges/graphs as input.

Like dict.update, this method takes a graph as input, adding the graph’s nodes and edges to this graph. It can also take two inputs: edges and nodes. Finally it can take either edges or nodes. To specify only nodes the keyword nodes must be used.

The collections of edges and nodes are treated similarly to the add_edges_from/add_nodes_from methods. When iterated, they should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).

Parameters:

edges (Graph object, collection of edges, or None) – The first parameter can be a graph or some edges. If it has attributes nodes and edges, then it is taken to be a Graph-like object and those attributes are used as collections of nodes and edges to be added to the graph. If the first parameter does not have those attributes, it is treated as a collection of edges and added to the graph. If the first argument is None, no edges are added.
nodes (collection of nodes, or None) – The second parameter is treated as a collection of nodes to be added to the graph unless it is None. If edges is None and nodes is None an exception is raised. If the first parameter is a Graph, then nodes is ignored.

Examples

>>> G = nx.path_graph(5)
>>> G.update(nx.complete_graph(range(4, 10)))
>>> from itertools import combinations
>>> edges = (
...     (u, v, {"power": u * v})
...     for u, v in combinations(range(10, 20), 2)
...     if u * v < 225
... )
>>> nodes = [1000]  # for singleton, use a container
>>> G.update(edges, nodes)

Notes

It you want to update the graph using an adjacency structure it is straightforward to obtain the edges/nodes from adjacency. The following examples provide common cases, your adjacency may be slightly different and require tweaks of these examples.

>>> # dict-of-set/list/tuple
>>> adj = {1: {2, 3}, 2: {1, 3}, 3: {1, 2}}
>>> e = [(u, v) for u, nbrs in adj.items() for v in nbrs]
>>> G.update(edges=e, nodes=adj)

>>> DG = nx.DiGraph()
>>> # dict-of-dict-of-attribute
>>> adj = {1: {2: 1.3, 3: 0.7}, 2: {1: 1.4}, 3: {1: 0.7}}
>>> e = [
...     (u, v, {"weight": d})
...     for u, nbrs in adj.items()
...     for v, d in nbrs.items()
... ]
>>> DG.update(edges=e, nodes=adj)

>>> # dict-of-dict-of-dict
>>> adj = {1: {2: {"weight": 1.3}, 3: {"color": 0.7, "weight": 1.2}}}
>>> e = [
...     (u, v, {"weight": d})
...     for u, nbrs in adj.items()
...     for v, d in nbrs.items()
... ]
>>> DG.update(edges=e, nodes=adj)

>>> # predecessor adjacency (dict-of-set)
>>> pred = {1: {2, 3}, 2: {3}, 3: {3}}
>>> e = [(v, u) for u, nbrs in pred.items() for v in nbrs]

>>> # MultiGraph dict-of-dict-of-dict-of-attribute
>>> MDG = nx.MultiDiGraph()
>>> adj = {
...     1: {2: {0: {"weight": 1.3}, 1: {"weight": 1.2}}},
...     3: {2: {0: {"weight": 0.7}}},
... }
>>> e = [
...     (u, v, ekey, d)
...     for u, nbrs in adj.items()
...     for v, keydict in nbrs.items()
...     for ekey, d in keydict.items()
... ]
>>> MDG.update(edges=e)

See also

add_edges_from: add multiple edges to a graph
add_nodes_from: add multiple nodes to a graph

get_node_from_name(name)[source]#: Return the first node that has a given name.

set_from_dict(config_dict, remove_existing_edges=False, remove_existing_nodes=False, clear_special_nodes=True)[source]#

Set the values of existing network attributes from those contained in a dictionary.

Parameters:

config_dict – A dictionary of network attribute name value pairs
remove_existing_edges – Whether to remove existing edges
remove_existing_nodes – Whether to remove existing nodes

clear_special_nodes()[source]#: Remove all special node designations.

set_node_positions(network_layout=NetworkLayout.SPRING)[source]#

Sets the Node positions of the current Network.

Parameters:: network_layout – A member of NetworkLayout. Default is NetworkLayout.SPRING.

add_nodes_from_dict(nodes_dict, remove_existing=False)[source]#

Add nodes to the graph with properties defined from a dictionary.

Parameters:

nodes_dict – a dictionary of node uuids to properties
remove_existing – a boolean to indicate whether to remove existing nodes

add_edges_from_dict(edges_dict, remove_existing=False)[source]#

Add edges to the graph with properties defined from a dictionary.

Parameters:

edges_dict – a dictionary of edge uuids to properties
remove_existing – a boolean to indicate whether to remove existing edges

reset_random_entry_nodes()[source]#

Set the entry nodes.

If no entry nodes supplied then the first node in the network is chosen as the initial node.

reset_random_high_value_nodes()[source]#

Sets up the high value nodes (HVNs) to be used by the training environment.

If HVNs are supplied in the Network, they are used. However, if they are not supplied, the following logic is applied:

If game_mode.game_rules.lose_when_high_value_node_lost is True:
An acceptable amount (math.ceil((len(current_graph.nodes) - len(entry_nodes) + 1) * 0.15) of HVNs are defined from a list of potential HVSn at random after steps are taken to ensure that HVNs are not entry nodes.

Otherwise:
HVNs are set to an empty list.

reset_random_vulnerabilities()[source]#: Regenerate random vulnerabilities for every node in the network.

to_dict(json_serializable=False)[source]#: Represent the Network as a dictionary.

to_adj_matrix_and_positions()[source]#: Represent the network by its adjacency matrix and a dictionary of node names to positions.

classmethod create(network_dict)[source]#

Create an instance on :class: Network from a dictionary.

Parameters:: network_dict – a dictionary describing a Network
Returns:: An instance of :class: Network.