def METHOD_NAME(shape, spacing=1, mode='kdtree', node_prefix='node', edge_prefix='edge'):
    r"""
    Generate a face-centered cubic lattice
    Parameters
    ----------
    shape : array_like
        The number of corner sites in each direction. A sipmle cubic lattice
        is created then the 'face-sites' are added afterwards.
    spacing : array_like or float
        The size of a unit cell in each direction. If an scalar is given it is
        applied in all 3 directions.
    mode : str
        Dictate how neighbors are found.  Options are:
        ===============  =====================================================
        mode             meaning
        ===============  =====================================================
        'kdtree'         Uses ``scipy.spatial.KDTree`` to find all neighbors
                         within the unit cell.
        'triangulation'  Uses ``scipy.spatial.Delaunay`` to find all neighbors.
        ===============  =====================================================
    Returns
    -------
    network : dict
        A dictionary containing 'coords', 'conns' and various boolean labels
        (i.e. 'node.center')
    Notes
    -----
    It is not clear whether KDTree of Delaunay are faster. In fact it is
    surely possible to find the neighbors formulaically but this is not
    implemented yet.
    """
    shape = np.array(shape)
    # Create base cubic network of corner sites
    net1 = cubic(shape=shape,
                 node_prefix=node_prefix, edge_prefix=edge_prefix)
    # Create 3 networks to become face sites
    net2 = cubic(shape=shape - [1, 1, 0],
                 node_prefix=node_prefix, edge_prefix=edge_prefix)
    net3 = cubic(shape=shape - [1, 0, 1],
                 node_prefix=node_prefix, edge_prefix=edge_prefix)
    net4 = cubic(shape=shape - [0, 1, 1],
                 node_prefix=node_prefix, edge_prefix=edge_prefix)
    # Offset pore coords by 1/2 a unit cell
    net2[node_prefix+'.coords'] += np.array([0.5, 0.5, 0])
    net3[node_prefix+'.coords'] += np.array([0.5, 0, 0.5])
    net4[node_prefix+'.coords'] += np.array([0, 0.5, 0.5])
    crds = np.concatenate((net1[node_prefix+'.coords'],
                           net2[node_prefix+'.coords'],
                           net3[node_prefix+'.coords'],
                           net4[node_prefix+'.coords']))
    corner_labels = np.concatenate(
        (np.ones(net1[node_prefix+'.coords'].shape[0], dtype=bool),
         np.zeros(net2[node_prefix+'.coords'].shape[0], dtype=bool),
         np.zeros(net3[node_prefix+'.coords'].shape[0], dtype=bool),
         np.zeros(net4[node_prefix+'.coords'].shape[0], dtype=bool)))
    if mode.startswith('tri'):
        tri = sptl.Delaunay(points=crds)
        am = tri_to_am(tri)
        conns = np.vstack((am.row, am.col)).T
        # Trim diagonal connections between cubic pores
        L = np.sqrt(np.sum(np.diff(crds[conns], axis=1)**2, axis=2)).flatten()
        conns = conns[L <= 0.75]
    elif mode.startswith('kd'):
        tree1 = sptl.KDTree(crds)
        # Method 1
        hits = tree1.query_ball_point(crds, r=.75)
        # Method 2: Not sure which is faster
        # tree2 = sptl.KDTree(crds)
        # hits = tree1.query_ball_tree(tree1, r=1)
        indices = np.hstack(hits)
        # Convert to CSR matrix
        indptr = [len(i) for i in hits]
        indptr.insert(0, 0)
        indptr = np.cumsum(indptr)
        am = sprs.csr_matrix((np.ones_like(indices), indices, indptr))
        am = sprs.triu(am, k=1)
        am = am.tocoo()
        conns = np.vstack((am.row, am.col)).T
    conns = np.vstack((net1[edge_prefix+'.conns'], conns))
    d = {}
    d[node_prefix + '.coords'] = crds*spacing
    d[edge_prefix + '.conns'] = conns
    d[node_prefix + '.corner'] = corner_labels
    d[node_prefix + '.face'] = ~corner_labels
    return d