How to use the networkx.NetworkXError function in networkx

-----
    This algorithm appears in [1].

    This implementation disallows the possibility of generating
    disconnected graphs.

    References
    ----------
    .. [1] I. Ispolatov, P. L. Krapivsky, A. Yuryev,
       "Duplication-divergence model of protein interaction network",
       Phys. Rev. E, 71, 061911, 2005.

    """
    if p > 1 or p < 0:
        msg = "NetworkXError p={0} is not in [0,1].".format(p)
        raise nx.NetworkXError(msg)
    if n < 2:
        msg = 'n must be greater than or equal to 2'
        raise nx.NetworkXError(msg)

    G = nx.Graph()

    # Initialize the graph with two connected nodes.
    G.add_edge(0, 1)
    i = 2
    while i < n:
        # Choose a random node from current graph to duplicate.
        random_node = seed.choice(list(G))
        # Make the replica.
        G.add_node(i)
        # flag indicates whether at least one edge is connected on the replica.
        flag = False

def decode_attr_elements(self, gexf_keys, obj_xml):
        # Use the key information to decode the attr XML
        attr = {}
        # look for outer "" element
        attr_element=obj_xml.find("{%s}attvalues" % self.NS_GEXF)
        if attr_element is not None:
            # loop over  elements
            for a in attr_element.findall("{%s}attvalue" % self.NS_GEXF):
                key = a.get('for') # for is required
                try: # should be in our gexf_keys dictionary
                    title=gexf_keys[key]['title']
                except KeyError:
                    raise nx.NetworkXError("No attribute defined for=%s"%key)
                atype=gexf_keys[key]['type']
                value=a.get('value')
                if atype=='boolean':
                    value=self.convert_bool[value]
                else:
                    value=self.python_type[atype](value)
                if gexf_keys[key]['mode']=='dynamic':
                    # for dynamic graphs use list of three-tuples
                    # [(value1,start1,end1), (value2,start2,end2), etc]
                    start=a.get('start')
                    end=a.get('end')
                    if title in attr:
                        attr[title].append((value,start,end))
                    else:
                        attr[title]=[(value,start,end)]
                else:

def _sparse_fruchterman_reingold(A, dim=2, k=None, pos=None, fixed=None,
                                 iterations=50):
    # Position nodes in adjacency matrix A using Fruchterman-Reingold
    # Entry point for NetworkX graph is fruchterman_reingold_layout()
    # Sparse version
    import numpy as np
    try:
        nnodes,_=A.shape
    except AttributeError:
        raise nx.NetworkXError(
            "fruchterman_reingold() takes an adjacency matrix as input")
    try:
        from scipy.sparse import spdiags,coo_matrix
    except ImportError:
        raise ImportError("_sparse_fruchterman_reingold() scipy numpy: http://scipy.org/ ")
    # make sure we have a LIst of Lists representation
    try:
        A=A.tolil()
    except:
        A=(coo_matrix(A)).tolil()

    if pos is None:
        # random initial positions
        pos=np.asarray(np.random.random((nnodes,dim)),dtype=A.dtype)
    else:
        # make sure positions are of same type as matrix

def ford_fulkerson_impl(G, s, t, capacity):
    """Legacy implementation of the Edmonds-Karp algorithm"""
    if G.is_multigraph():
        raise nx.NetworkXError(
                'MultiGraph and MultiDiGraph not supported (yet).')

    if s not in G:
        raise nx.NetworkXError('node %s not in graph' % str(s))
    if t not in G:
        raise nx.NetworkXError('node %s not in graph' % str(t))
    if s == t:
        raise nx.NetworkXError('source and sink are the same node')

    auxiliary = _create_auxiliary_digraph(G, capacity=capacity)
    inf_capacity_flows = auxiliary.graph['inf_capacity_flows']

    flow_value = 0   # Initial feasible flow.

    # As long as there is an (s, t)-path in the auxiliary digraph, find
    # the shortest (with respect to the number of arcs) such path and

Notes
    -----

    The initial graph `G` must be connected, and the resulting graph is
    connected. The graph `G` is modified in place.

    References
    ----------
    .. [1] C. Gkantsidis and M. Mihail and E. Zegura,
           The Markov chain simulation method for generating connected
           power law random graphs, 2003.
           http://citeseer.ist.psu.edu/gkantsidis03markov.html
    """
    if not nx.is_connected(G):
        raise nx.NetworkXError("Graph not connected")
    if len(G) < 4:
        raise nx.NetworkXError("Graph has less than four nodes.")
    n = 0
    swapcount = 0
    deg = G.degree()
    # Label key for nodes
    dk = list(n for n, d in G.degree())
    cdf = nx.utils.cumulative_distribution(list(d for n, d in G.degree()))
    discrete_sequence = nx.utils.discrete_sequence
    window = 1
    while n < nswap:
        wcount = 0
        swapped = []
        # If the window is small, we just check each time whether the graph is
        # connected by checking if the nodes that were just separated are still
        # connected.

-----

    The initial graph ``G`` must be connected, and the resulting graph is
    connected. The graph ``G`` is modified in place.

    References
    ----------
    .. [1] C. Gkantsidis and M. Mihail and E. Zegura,
           The Markov chain simulation method for generating connected
           power law random graphs, 2003.
           http://citeseer.ist.psu.edu/gkantsidis03markov.html
    """
    if not nx.is_connected(G):
        raise nx.NetworkXError("Graph not connected")
    if len(G) < 4:
        raise nx.NetworkXError("Graph has less than four nodes.")
    n = 0
    swapcount = 0
    deg = G.degree()
    # Label key for nodes
    dk = list(deg.keys())
    cdf = nx.utils.cumulative_distribution(list(G.degree().values()))
    window = 1
    while n < nswap:
        wcount = 0
        swapped = []
        # If the window is small, we just check each time whether the graph is
        # connected by checking if the nodes that were just separated are still
        # connected.
        if window < _window_threshold:
            # This Boolean keeps track of whether there was a failure or not.
            fail = False

def _triangles(G, e):
    """ Return list of all triangles containing edge e"""
    u, v = e
    if u not in G:
        raise nx.NetworkXError("Vertex %s not in graph" % u)
    if v not in G[u]:
        raise nx.NetworkXError("Edge (%s, %s) not in graph" % (u, v))
    triangle_list = []
    for x in G[u]:
        if x in G[v]:
            triangle_list.append((u, v, x))
    return triangle_list

def decode_data_elements(self, graphml_keys, obj_xml):
        """Use the key information to decode the data XML if present."""
        data = {}
        for data_element in obj_xml.findall("{%s}data" % self.NS_GRAPHML):
            key = data_element.get("key")
            try:
                data_name=graphml_keys[key]['name']
                data_type=graphml_keys[key]['type']
            except KeyError:
                raise nx.NetworkXError("Bad GraphML data: no key %s"%key)
            text=data_element.text
            # assume anything with subelements is a yfiles extension
            if text is not None and len(list(data_element))==0:
                if data_type==bool:
                    data[data_name] = self.convert_bool[text]
                else:
                    data[data_name] = data_type(text)
            elif len(list(data_element)) > 0:
                # Assume yfiles as subelements, try to extract node_label
                node_label = None
                for node_type in ['ShapeNode', 'SVGNode', 'ImageNode']:
                    geometry = data_element.find("{%s}%s/{%s}Geometry" %
                                (self.NS_Y, node_type, self.NS_Y))
                    if geometry is not None:
                        data['x'] = geometry.get('x')
                        data['y'] = geometry.get('y')

--------
    global_reaching_centrality

    References
    ----------
    .. [1] Mones, Enys, Lilla Vicsek, and Tamás Vicsek.
           "Hierarchy Measure for Complex Networks."
           *PLoS ONE* 7.3 (2012): e33799.
           https://doi.org/10.1371/journal.pone.0033799
    """
    if paths is None:
        if nx.is_negatively_weighted(G, weight=weight):
            raise nx.NetworkXError('edge weights must be positive')
        total_weight = G.size(weight=weight)
        if total_weight <= 0:
            raise nx.NetworkXError('Size of G must be positive')
        if weight is not None:
            # Interpret weights as lengths.
            def as_distance(u, v, d): return total_weight / d.get(weight, 1)
            paths = nx.shortest_path(G, source=v, weight=as_distance)
        else:
            paths = nx.shortest_path(G, source=v)
    # If the graph is unweighted, simply return the proportion of nodes
    # reachable from the source node ``v``.
    if weight is None and G.is_directed():
        return (len(paths) - 1) / (len(G) - 1)
    if normalized and weight is not None:
        norm = G.size(weight=weight) / G.size()
    else:
        norm = 1
    # TODO This can be trivially parallelized.
    avgw = (_average_weight(G, path, weight=weight) for path in paths.values())

nodes=v
    else:                      # assume it is a single value
        nodes=[v]
    order=G.order()

    e={}
    for v in nodes:
        if sp is None:
            length=networkx.single_source_shortest_path_length(G,v)
            L = len(length)
        else:
            try:
                length=sp[v]
                L = len(length)
            except TypeError:
                raise networkx.NetworkXError('Format of "sp" is invalid.')
        if L != order:
            msg = "Graph not connected: infinite path length"
            raise networkx.NetworkXError(msg)
            
        e[v]=max(length.values())

    if len(e)==1: 
        return list(e.values())[0] # return single value
    else:
        return e