def resistance_distance(self, other_kg, **kwargs):
""" A NetComp operation returning the resistance distance between `self` and `other_kg`.
See NetComp's docs for keyword arguments: https://github.com/peterewills/NetComp/blob/master/netcomp/distance/exact.py#L225
:param other_kg: The other knowledge graph
:type other_kg: :class:`.KnowledgeGraph`
:return: The resistance distance of `self` and `other_kg`. If the kwarg `attributed` is True (default=False),
then the vector distance per node is returned
:rtype: float, :class:`numpy.array`
"""
A, B = make_adjacency_matrices(self, other_kg)
return nc.resistance_distance(A, B, **kwargs)
G = nx.grid_2d_graph(N, M)
deg_seq = [item[1] for item in G.degree_iter()]
####################################
## DEFINE IMPORTANT FUNCTIONS
####################################
def distance(dist_func, A, B):
return dist_func(A, B)
lambda_adj = lambda A1, A2: nc.lambda_dist(A1, A2, kind="adjacency")
lambda_lap = lambda A1, A2: nc.lambda_dist(A1, A2, kind="laplacian")
lambda_nlap = lambda A1, A2: nc.lambda_dist(A1, A2, kind="laplacian_norm")
res_dist = lambda A1, A2: nc.resistance_distance(A1, A2, check_connected=False)
distances = [
nc.edit_distance,
res_dist,
nc.deltacon0,
nc.netsimile,
lambda_adj,
lambda_lap,
lambda_nlap,
]
labels = [
"Edit",
"Resistance Dist.",
"DeltaCon",
"NetSimile",
if n != m:
raise ValueError("Input matrix must be square.")
inds = list(range(n))
random.shuffle(inds)
A_shuff = A[np.ix_(inds, inds)]
return A_shuff
def distance(dist_func, A, B):
return dist_func(A, B)
lambda_adj = lambda A1, A2: nc.lambda_dist(A1, A2, kind="adjacency")
lambda_lap = lambda A1, A2: nc.lambda_dist(A1, A2, kind="laplacian")
lambda_nlap = lambda A1, A2: nc.lambda_dist(A1, A2, kind="laplacian_norm")
res_dist = lambda A1, A2: nc.resistance_distance(A1, A2, renormalized=True)
distances = [
nc.edit_distance,
res_dist,
nc.deltacon0,
nc.netsimile,
lambda_adj,
lambda_lap,
lambda_nlap,
]
labels = [
"Edit",
"Resistance Dist. (Renormalized)",
"DeltaCon",
"NetSimile",
# A trivial example, for now import netcomp as nc import networkx as nx # use networkx to build a sample graph G0 = nx.complete_graph(10) G1 = G0.copy() G1.remove_edge(1, 0) G2 = G0.copy() G2.remove_node(0) A, B, C = [nx.adjacency_matrix(G) for G in [G0, G1, G2]] # matrix distances nc.resistance_distance(A, B) nc.edit_distance(A, B) nc.deltacon0(A, B) nc.vertex_edge_distance(A, B) # spectral distances nc.lambda_dist(A, C) nc.lambda_dist(A, C, kind='adjacency', k=2) nc.lambda_dist(A, C, kind='laplacian_norm') # other distances nc.resistance_distance(A, C, renormalized=True) nc.netsimile(A, C) # matrices w/o associated distances nc.commute_matrix(A) nc.conductance_matrix(A)