Python networkx.all_neighbors() Examples

def ramsey_R2(G):
    r"""Approximately computes the Ramsey number `R(2;s,t)` for graph.

    Parameters
    ----------
    G : NetworkX graph
        Undirected graph

    Returns
    -------
    max_pair : (set, set) tuple
        Maximum clique, Maximum independent set.
    """
    if not G:
        return (set([]), set([]))

    node = next(G.nodes_iter())
    nbrs = nx.all_neighbors(G, node)
    nnbrs = nx.non_neighbors(G, node)
    c_1, i_1 = ramsey_R2(G.subgraph(nbrs))
    c_2, i_2 = ramsey_R2(G.subgraph(nnbrs))

    c_1.add(node)
    i_2.add(node)
    return (max([c_1, c_2]), max([i_1, i_2])) 

def normalized_mutual_weight(G, u, v, norm=sum, weight=None):
    """Returns normalized mutual weight of the edges from `u` to `v`
    with respect to the mutual weights of the neighbors of `u` in `G`.

    `norm` specifies how the normalization factor is computed. It must
    be a function that takes a single argument and returns a number.
    The argument will be an iterable of mutual weights
    of pairs ``(u, w)``, where ``w`` ranges over each (in- and
    out-)neighbor of ``u``. Commons values for `normalization` are
    ``sum`` and ``max``.

    `weight` can be ``None`` or a string, if None, all edge weights
    are considered equal. Otherwise holds the name of the edge
    attribute used as weight.

    """
    scale = norm(mutual_weight(G, u, w, weight)
                 for w in set(nx.all_neighbors(G, u)))
    return 0 if scale == 0 else mutual_weight(G, u, v, weight) / scale 

def normalized_mutual_weight(G, u, v, norm=sum, weight=None):
    """Returns normalized mutual weight of the edges from `u` to `v`
    with respect to the mutual weights of the neighbors of `u` in `G`.

    `norm` specifies how the normalization factor is computed. It must
    be a function that takes a single argument and returns a number.
    The argument will be an iterable of mutual weights
    of pairs ``(u, w)``, where ``w`` ranges over each (in- and
    out-)neighbor of ``u``. Commons values for `normalization` are
    ``sum`` and ``max``.

    `weight` can be ``None`` or a string, if None, all edge weights
    are considered equal. Otherwise holds the name of the edge
    attribute used as weight.

    """
    scale = norm(mutual_weight(G, u, w, weight)
                 for w in set(nx.all_neighbors(G, u)))
    return 0 if scale == 0 else mutual_weight(G, u, v, weight) / scale 

def secondary_hops_added(self, node_pub_key):
        """
        Determines the number of secondary hops added if connected to the node.

        :param node_pub_key: str
        :return: int
        """
        potential_new_second_neighbors = set(
            nx.all_neighbors(self.node.network.graph, node_pub_key))
        current_close_neighbors = set(
            self.get_nodes_n_hops_away(self.node.pub_key, 2).keys())
        new_second_neighbors = potential_new_second_neighbors.difference(
            current_close_neighbors)
        return len(new_second_neighbors) 

def nodes_most_second_neighbors(self, node_pub_key, number_of_nodes=10):
        """
        Which node should be added in order to reach the most other nodes
        with two hops?

        :param node_pub_key: string
        :param number_of_nodes: int
        :return: list of results nodes, adding the most secondary neighbors
        """

        node_candidates = []

        # set of nodes currently two hops away
        current_close_neighbors = set(
            self.get_nodes_n_hops_away(node_pub_key, 2).keys())

        # loop through nodes in the network and check their direct neighbors
        for n in self.node.network.graph:
            potential_new_second_neighbors = set(
                nx.all_neighbors(self.node.network.graph, n))

            # subtract current_close_neighbors from
            # the potential new second neighbors
            new_second_neighbors = potential_new_second_neighbors.difference(
                current_close_neighbors)

            # add the node and its number of secondary neighbors to a list
            node_candidates.append([n, len(new_second_neighbors)])

        nodes_sorted = sorted(
            node_candidates, key=lambda x: x[1], reverse=True)

        return nodes_sorted[:number_of_nodes] 

def find_neighbors(cite_graph):

    print ("find neighbors ....")
    indx_neighbors = {}
    for node in cite_graph.nodes():
        indx_neighbors.setdefault(node,[]).extend(list(nx.all_neighbors(cite_graph,node)))
    with codecs.open("./raw_data/indx_neighbors.json","w","utf-8") as fid:
        json.dump(indx_neighbors,fid) 

def ramsey_R2(G):
    r"""Approximately computes the Ramsey number `R(2;s,t)` for graph.

    Parameters
    ----------
    G : NetworkX graph
        Undirected graph

    Returns
    -------
    max_pair : (set, set) tuple
        Maximum clique, Maximum independent set.
    """
    if not G:
        return set(), set()

    node = arbitrary_element(G)
    nbrs = nx.all_neighbors(G, node)
    nnbrs = nx.non_neighbors(G, node)
    c_1, i_1 = ramsey_R2(G.subgraph(nbrs).copy())
    c_2, i_2 = ramsey_R2(G.subgraph(nnbrs).copy())

    c_1.add(node)
    i_2.add(node)
    # Choose the larger of the two cliques and the larger of the two
    # independent sets, according to cardinality.
    return max(c_1, c_2, key=len), max(i_1, i_2, key=len) 

def ramsey_R2(G):
    r"""Approximately computes the Ramsey number `R(2;s,t)` for graph.

    Parameters
    ----------
    G : NetworkX graph
        Undirected graph

    Returns
    -------
    max_pair : (set, set) tuple
        Maximum clique, Maximum independent set.
    """
    if not G:
        return set(), set()

    node = arbitrary_element(G)
    nbrs = nx.all_neighbors(G, node)
    nnbrs = nx.non_neighbors(G, node)
    c_1, i_1 = ramsey_R2(G.subgraph(nbrs).copy())
    c_2, i_2 = ramsey_R2(G.subgraph(nnbrs).copy())

    c_1.add(node)
    i_2.add(node)
    # Choose the larger of the two cliques and the larger of the two
    # independent sets, according to cardinality.
    return max(c_1, c_2, key=len), max(i_1, i_2, key=len) 

def functionalityScan(graphity, pattern):
	
	# search is performed by defining "anchor" node, where initial pattern is found
	# search then moved from there 1 level up to search surrounding nodes (number of levels could be increased)
	# pattern lists for now are kept rather small
	# TODO determine distance between found patterns to see which functionalities lie close to each other
	patternNum = len(pattern)
	anchorList = []
	
	allCalls = nx.get_node_attributes(graphity, 'calls')
	
	for function in allCalls:
		for call in allCalls[function]:
			
			api = call[1]
			anchorpat = pattern[0]
			
			if anchorpat in api: 
				if not filter(lambda daAnchor: daAnchor['address'] == function, anchorList):
					
					# maintain a dict of patterns per anchor to keep track of found patterns
					patternCheck = {}
					for item in pattern:
						patternCheck[item] = False
					patternCheck[anchorpat] = function
					
					anchorList.append({'address':function, 'patterns':patternCheck})

	# anchor nodes found and more than one pattern searched for							
	if patternNum > 1 and len(anchorList) > 0:
		for anchor in anchorList:
		
			scanNodeForApi(anchor, anchor['address'], patternNum)
			if False in anchor['patterns'].values():
			
				anchorNeighbors = nx.all_neighbors(graphity, anchor['address'])
				for neighbor in anchorNeighbors:
					scanNodeForApi(anchor, neighbor, patternNum)
			
	return anchorList
				

# Search for a specific pattern within a node, orient by anchor pattern 

def constraint(G, nodes=None, weight=None):
    r"""Returns the constraint on all nodes in the graph ``G``.

    The *constraint* is a measure of the extent to which a node *v* is
    invested in those nodes that are themselves invested in the
    neighbors of *v*. Formally, the *constraint on v*, denoted `c(v)`,
    is defined by

    .. math::

       c(v) = \sum_{w \in N(v) \setminus \{v\}} \ell(v, w)

    where `N(v)` is the subset of the neighbors of `v` that are either
    predecessors or successors of `v` and `\ell(v, w)` is the local
    constraint on `v` with respect to `w` [1]_. For the definition of local
    constraint, see :func:`local_constraint`.

    Parameters
    ----------
    G : NetworkX graph
        The graph containing ``v``. This can be either directed or undirected.

    nodes : container, optional
        Container of nodes in the graph ``G`` to compute the constraint. If
        None, the constraint of every node is computed.

    weight : None or string, optional
      If None, all edge weights are considered equal.
      Otherwise holds the name of the edge attribute used as weight.

    Returns
    -------
    dict
        Dictionary with nodes as keys and the constraint on the node as values.

    See also
    --------
    local_constraint

    References
    ----------
    .. [1] Burt, Ronald S.
           "Structural holes and good ideas".
           American Journal of Sociology (110): 349–399.

    """
    if nodes is None:
        nodes = G
    constraint = {}
    for v in nodes:
        # Constraint is not defined for isolated nodes
        if len(G[v]) == 0:
            constraint[v] = float('nan')
            continue
        constraint[v] = sum(local_constraint(G, v, n, weight)
                            for n in set(nx.all_neighbors(G, v)))
    return constraint 

def local_constraint(G, u, v, weight=None):
    r"""Returns the local constraint on the node ``u`` with respect to
    the node ``v`` in the graph ``G``.

    Formally, the *local constraint on u with respect to v*, denoted
    $\ell(v)$, is defined by

    .. math::

       \ell(u, v) = \left(p_{uv} + \sum_{w \in N(v)} p_{uw} p{wv}\right)^2,

    where $N(v)$ is the set of neighbors of $v$ and $p_{uv}$ is the
    normalized mutual weight of the (directed or undirected) edges
    joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. The *mutual
    weight* of $u$ and $v$ is the sum of the weights of edges joining
    them (edge weights are assumed to be one if the graph is
    unweighted).

    Parameters
    ----------
    G : NetworkX graph
        The graph containing ``u`` and ``v``. This can be either
        directed or undirected.

    u : node
        A node in the graph ``G``.

    v : node
        A node in the graph ``G``.

    weight : None or string, optional
      If None, all edge weights are considered equal.
      Otherwise holds the name of the edge attribute used as weight.

    Returns
    -------
    float
        The constraint of the node ``v`` in the graph ``G``.

    See also
    --------
    constraint

    References
    ----------
    .. [1] Burt, Ronald S.
           "Structural holes and good ideas".
           American Journal of Sociology (110): 349–399.

    """
    nmw = normalized_mutual_weight
    direct = nmw(G, u, v, weight=weight)
    indirect = sum(nmw(G, u, w, weight=weight) * nmw(G, w, v, weight=weight)
                   for w in set(nx.all_neighbors(G, u)))
    return (direct + indirect) ** 2 

def constraint(G, nodes=None, weight=None):
    r"""Returns the constraint on all nodes in the graph ``G``.

    The *constraint* is a measure of the extent to which a node *v* is
    invested in those nodes that are themselves invested in the
    neighbors of *v*. Formally, the *constraint on v*, denoted `c(v)`,
    is defined by

    .. math::

       c(v) = \sum_{w \in N(v) \setminus \{v\}} \ell(v, w)

    where `N(v)` is the subset of the neighbors of `v` that are either
    predecessors or successors of `v` and `\ell(v, w)` is the local
    constraint on `v` with respect to `w` [1]_. For the definition of local
    constraint, see :func:`local_constraint`.

    Parameters
    ----------
    G : NetworkX graph
        The graph containing ``v``. This can be either directed or undirected.

    nodes : container, optional
        Container of nodes in the graph ``G`` to compute the constraint. If
        None, the constraint of every node is computed.

    weight : None or string, optional
      If None, all edge weights are considered equal.
      Otherwise holds the name of the edge attribute used as weight.

    Returns
    -------
    dict
        Dictionary with nodes as keys and the constraint on the node as values.

    See also
    --------
    local_constraint

    References
    ----------
    .. [1] Burt, Ronald S.
           "Structural holes and good ideas".
           American Journal of Sociology (110): 349–399.

    """
    if nodes is None:
        nodes = G
    constraint = {}
    for v in nodes:
        # Constraint is not defined for isolated nodes
        if len(G[v]) == 0:
            constraint[v] = float('nan')
            continue
        constraint[v] = sum(local_constraint(G, v, n, weight)
                            for n in set(nx.all_neighbors(G, v)))
    return constraint 

def local_constraint(G, u, v, weight=None):
    r"""Returns the local constraint on the node ``u`` with respect to
    the node ``v`` in the graph ``G``.

    Formally, the *local constraint on u with respect to v*, denoted
    $\ell(v)$, is defined by

    .. math::

       \ell(u, v) = \left(p_{uv} + \sum_{w \in N(v)} p_{uw} p{wv}\right)^2,

    where $N(v)$ is the set of neighbors of $v$ and $p_{uv}$ is the
    normalized mutual weight of the (directed or undirected) edges
    joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. The *mutual
    weight* of $u$ and $v$ is the sum of the weights of edges joining
    them (edge weights are assumed to be one if the graph is
    unweighted).

    Parameters
    ----------
    G : NetworkX graph
        The graph containing ``u`` and ``v``. This can be either
        directed or undirected.

    u : node
        A node in the graph ``G``.

    v : node
        A node in the graph ``G``.

    weight : None or string, optional
      If None, all edge weights are considered equal.
      Otherwise holds the name of the edge attribute used as weight.

    Returns
    -------
    float
        The constraint of the node ``v`` in the graph ``G``.

    See also
    --------
    constraint

    References
    ----------
    .. [1] Burt, Ronald S.
           "Structural holes and good ideas".
           American Journal of Sociology (110): 349–399.

    """
    nmw = normalized_mutual_weight
    direct = nmw(G, u, v, weight=weight)
    indirect = sum(nmw(G, u, w, weight=weight) * nmw(G, w, v, weight=weight)
                   for w in set(nx.all_neighbors(G, u)))
    return (direct + indirect) ** 2