def exhaustive_set(G, query_nodes, target_nodes, n_edges, start_dist):
"""Exaustively searches all the combinations of k links between
a set of query nodes Q and a set of absorbing
target nodes C such that Q \cap C = \emptyset.
Parameters
----------
G : Networkx graph
The graph from which the team will be selected.
query : list
The set of nodes from which random walker starts.
target : list
The set of nodes from where the random walker ends.
n_edges : integer
the number of links to be added
start_dist: list
The starting distribution over the query set
Returns
-------
links : list
The set of links that reduce the absorbing RW centrality
ac_scores: list
The set of scores of adding the links
"""
query_set_size = len(query_nodes)
map_query_to_org = dict(zip(query_nodes, range(query_set_size)))
P = csc_matrix(nx.google_matrix(G, alpha=1))
P_abs = P[list(query_nodes),:][:,list(query_nodes)]
F = compute_fundamental(P_abs)
row_sums = start_dist.dot(F.sum())[0,0]
candidates = list(product(query_nodes, target_nodes))
eligible = [candidates[i] for i in range(len(candidates))
if G.has_edge(candidates[i][0], candidates[i][1]) == False]
ac_scores = [row_sums]
exhaustive_links = []
for L in range(1, n_edges+1):
print '\t Number of edges {}'.format(L)
round_min = -1
best_combination = []
for subset in combinations(eligible, L):
H = G.copy()
F_modified = F.copy()
for links_to_add in subset:
F_updated = update_fundamental_mat(F_modified, H, map_query_to_org, links_to_add[0])
H.add_edge(links_to_add[0], links_to_add[1])
F_modified = F_updated
abs_cen = start_dist.dot( F_updated.sum(axis = 1))[0,0]
if abs_cen < round_min or round_min == -1:
best_combination = subset
round_min = abs_cen
exhaustive_links.append(best_combination)
ac_scores.append(round_min)
return exhaustive_links, ac_scores
def random_links(G, query_nodes, target_nodes, n_edges, start_dist):
"""Selects a random set of links between a set of query nodes Q and a set of absorbing
target nodes C such that Q \cap C = \emptyset.
Parameters
----------
G : Networkx graph
The graph from which the team will be selected.
query : list
The set of nodes from which random walker starts.
target : list
The set of nodes from where the random walker ends.
n_edges : integer
the number of links to be added
start_dist: list
The starting distribution over the query set
Returns
-------
links : list
The set of links that reduce the absorbing RW centrality
ac_scores: list
The set of scores of adding the links
"""
query_set_size = len(query_nodes)
map_query_to_org = dict(zip(query_nodes, range(query_set_size)))
P = csc_matrix(nx.google_matrix(G, alpha=1))
P_abs = P[list(query_nodes),:][:,list(query_nodes)]
F = compute_fundamental(P_abs)
row_sums = start_dist.dot(F.sum())[0,0]
candidates = list(product(query_nodes, target_nodes))
eligible = [candidates[i] for i in range(len(candidates))
if G.has_edge(candidates[i][0], candidates[i][1]) == False]
links_to_add = sample(eligible, n_edges)
ac_scores = []
ac_scores.append(row_sums)
i = 0
while i < n_edges:
F_updated = update_fundamental_mat(F, G, map_query_to_org, links_to_add[i][0])
G.add_edge(links_to_add[i][0], links_to_add[i][1])
abs_cen = start_dist.dot(F_updated.sum(axis = 1))[0,0]
F = F_updated
ac_scores.append(abs_cen)
i += 1
return links_to_add, ac_scores
def get_approx_boundary(G, query_nodes, target_nodes, n_edges, start_dist):
"""
Used to calculate an approximation guarantee for greedy algorithm
"""
H = G.copy() # GET A COPY OF THE GRAPH
query_set_size = len(query_nodes)
target_set_size = len(target_nodes)
map_query_to_org = dict(zip(query_nodes, range(query_set_size)))
candidates = list(product(query_nodes, target_nodes))
# ALL minus exitsting in G
eligible = [candidates[i] for i in range(len(candidates))
if H.has_edge(candidates[i][0], candidates[i][1]) == False]
# CALCULATE MARGINAL GAIN TO EMPTY SET FOR ALL NODES IN STEEPNESS FUNCTION
P = csc_matrix(nx.google_matrix(H, alpha=1))
P_abs = P[list(query_nodes),:][:,list(query_nodes)]
F = compute_fundamental(P_abs)
row_sums_empty = start_dist.dot(F.sum(axis=1))[0,0] # F(\emptyset)
# candidates = list(product(query_nodes, target_nodes))
ac_marginal_empty = []
ac_marginal_full = []
source_idx_empty = []
node_processed = -1
for out_edge in eligible:
abs_cen = -1
source_node = out_edge[0]
if(node_processed == source_node):
# skip updating matrix because this updates the F matrix in the same way
continue
node_processed = source_node
F_updated = update_fundamental_mat(F, H, map_query_to_org, source_node)
abs_cen = start_dist.dot(F_updated.sum(axis = 1))[0,0]
ac_marginal_empty.append(abs_cen)
source_idx_empty.append(source_node)
sorted_indexes_empty = [i[0] for i in sorted(enumerate(source_idx_empty), key=lambda x:x[1])]
ac_marginal_empty = [ac_marginal_empty[i] for i in sorted_indexes_empty]
# CALCULATE MARGINAL GAIN FOR FULL SET
H.add_edges_from(eligible)
P_all = csc_matrix(nx.google_matrix(H, alpha=1))
P_abs_all = P_all[list(query_nodes),:][:,list(query_nodes)]
F_all = compute_fundamental(P_abs_all)
row_sums_all = start_dist.dot(F_all.sum(axis=1))[0,0]
node_prcessed = -1
source_idx = []
for out_edge in eligible:
abs_cen = -1
source_node = out_edge[0]
if(node_prcessed == source_node):
# skip updating matrix because this updates the F matrix in the same way
continue
node_prcessed = source_node
F_all_updated = update_rev_fundamental_mat(F_all, H, map_query_to_org, source_node)
abs_cen = start_dist.dot(F_all_updated.sum(axis = 1))[0,0]
ac_marginal_full.append(abs_cen)
source_idx.append(source_node)
sorted_indexes = [i[0] for i in sorted(enumerate(source_idx), key=lambda x:x[1])]
ac_marginal_full = [ac_marginal_full[i] for i in sorted_indexes]
assert sorted_indexes == sorted_indexes_empty , "Something is wrong with the way scores are appended"
all_steepness = (asarray(ac_marginal_full) - row_sums_all) / (row_sums_empty-asarray(ac_marginal_empty))
s = min(all_steepness)
node_max = argmin(all_steepness)
return 1-s, sorted_indexes[node_max]
def greedy_navigation(G, query_nodes, target_nodes, n_edges, start_dist):
"""Selects a set of links with a greedy descent algorithm that reduce the
absorbing RW centrality between a set of query nodes Q and a set of absorbing
target nodes C such that Q \cap C = \emptyset. The query and target set
must be a 'viable' partition of the graph.
Parameters
----------
G : Networkx graph
The graph from which the team will be selected.
query : list
The set of nodes from which random walker starts.
target : list
The set of nodes from where the random walker ends.
n_edges : integer
the number of links to be added
start_dist: list
The starting distribution over the query set
P : Scipy matrix
The transition matrix of the graph G
F : Scipy matrix
The fundamental matrix for the graph G with the given set of absorbing
random walk nodes
Returns
-------
links : list
The set of links that reduce the absorbing RW centrality
"""
H = G.copy()
prng = RandomState()
query_set_size = len(query_nodes)
target_set_size = len(target_nodes)
map_query_to_org = dict(zip(query_nodes, range(query_set_size)))
P = csc_matrix(nx.google_matrix(H, alpha=1))
P_abs = P[list(query_nodes),:][:,list(query_nodes)]
F = compute_fundamental(P_abs)
row_sums = start_dist.dot(F.sum(axis=1))[0,0]
best_F = zeros(F.shape)
optimal_set = []
ac_scores = []
ac_scores.append(row_sums)
while n_edges > 0:
round_min = -1
best_node = -1
for i in query_nodes:
abs_neighbours = [l for l in H.neighbors(i) if l in target_nodes]
if len(abs_neighbours) == target_set_size:
continue
F_updated = update_fundamental_mat(F, H, map_query_to_org, i)
abs_cen = start_dist.dot( F_updated.sum(axis = 1))[0,0]
if abs_cen < round_min or round_min == -1:
best_node = i
round_min = abs_cen
best_F = F_updated
F = best_F
ac_scores.append(round_min)
optimal_candidate_edges = [(best_node, k, round_min)
for k in target_nodes
if H.has_edge(best_node, k) == False ]
try:
edge_idx = prng.randint(0, len(optimal_candidate_edges))
except ValueError:
print(H.neighbors(best_node))
print([l for l in H.neighbors(best_node) if l in target_nodes])
print(best_node)
print(optimal_candidate_edges)
print(target_nodes)
H.add_edge(optimal_candidate_edges[edge_idx][0],
optimal_candidate_edges[edge_idx][1])
optimal_set.append(optimal_candidate_edges[edge_idx])
n_edges -= 1
return optimal_set, ac_scores
def reverse_greedy(G, query_nodes, target_nodes, n_edges, start_dist):
"""Selects a set of links with a reverse greedy descent algorithm that reduce the
absorbing RW centrality between a set of query nodes Q and a set of absorbing
target nodes C such that Q \cap C = \emptyset. The query and target set
must be a 'viable' partition of the graph.
Parameters
----------
G : Networkx graph
The graph from which the team will be selected.
query : list
The set of nodes from which random walker starts.
target : list
The set of nodes from where the random walker ends.
n_edges : integer
the number of links to be added
start_dist: list
The starting distribution over the query set
P : Scipy matrix
The transition matrix of the graph G
F : Scipy matrix
The fundamental matrix for the graph G with the given set of absorbing
random walk nodes
Returns
-------
links : list
The set of links that reduce the absorbing RW centrality
"""
H = G.copy()
query_set_size = len(query_nodes)
map_query_to_org = dict(zip(query_nodes, range(query_set_size)))
candidates = list(product(query_nodes, target_nodes))
eligible = [candidates[i] for i in range(len(candidates))
if H.has_edge(candidates[i][0], candidates[i][1]) == False]
H.add_edges_from(eligible)
P = csc_matrix(nx.google_matrix(H, alpha=1))
P_abs = P[list(query_nodes),:][:,list(query_nodes)]
F = compute_fundamental(P_abs)
row_sums = start_dist.dot(F.sum(axis=1))[0,0]
# candidates = list(product(query_nodes, target_nodes))
worst_F = zeros(F.shape)
worst_set = []
optimal_set = []
ac_scores = []
# ac_scores.append(row_sums)
while len(eligible) > 0:
round_min = -1
worst_link = (-1,-1)
node_prcessed = -1
for out_edge in eligible:
source_node = out_edge[0]
if(node_prcessed == source_node):
# skip updating matrix because this updates the F matrix in the same way
continue
node_prcessed = source_node
F_updated = update_rev_fundamental_mat(F, H, map_query_to_org, source_node)
abs_cen = start_dist.dot(F_updated.sum(axis = 1))[0,0]
if abs_cen < round_min or round_min == -1:
worst_link = out_edge
round_min = abs_cen
worst_F = F_updated
F = worst_F
H.remove_edge(*worst_link)
worst_set.append(worst_link)
eligible.remove(worst_link)
if (len(eligible) <= n_edges):
ac_scores.append(round_min)
optimal_set.append(worst_link)
return list(reversed(optimal_set)), list(reversed(ac_scores))
def link_prediction(G, query_nodes, target_nodes, n_edges, start_dist, alg = "ra"):
"""Selects a random set of links between based on the scores calculated by
a standard link-prediction algorithm from networkx library
Parameters
----------
G : Networkx graph
The graph from which the team will be selected.
query : list
The set of nodes from which random walker starts.
target : list
The set of nodes from where the random walker ends.
n_edges : integer
the number of links to be added
start_dist: list
The starting distribution over the query set
alg: string
A string describing the link-prediction algorithm to be used
Returns
-------
links : list
The set of links that reduce the absorbing RW centrality
ac_scores: list
The set of scores of adding the links
"""
assert alg in ["ra", "pa", "jaccard", "aa"], "alg must be one of [\"ra\", \"pa\", \"jaccard\", \"aa\"]."
H = G.copy()
query_set_size = len(query_nodes)
map_query_to_org = dict(zip(query_nodes, range(query_set_size)))
P = csc_matrix(nx.google_matrix(H, alpha=1))
P_abs = P[list(query_nodes),:][:,list(query_nodes)]
F = compute_fundamental(P_abs)
row_sums = start_dist.dot(F.sum())[0,0]
candidates = list(product(query_nodes, target_nodes))
eligible = [candidates[i] for i in range(len(candidates))
if H.has_edge(candidates[i][0], candidates[i][1]) == False]
links_to_add = []
if alg == 'ra':
preds = nx.resource_allocation_index(H, eligible)
elif alg == 'jaccard':
preds = nx.jaccard_coefficient(H, eligible)
elif alg == 'aa':
preds = nx.adamic_adar_index(H, eligible)
elif alg == 'pa':
preds = nx.preferential_attachment(H, eligible)
for u,v,p in preds:
links_to_add.append((u,v,p))
links_to_add.sort(key=lambda x: x[2], reverse = True)
ac_scores = []
ac_scores.append(row_sums)
i = 0
while i < n_edges:
F_updated = update_fundamental_mat(F, H, map_query_to_org, links_to_add[i][0])
H.add_edge(links_to_add[i][0], links_to_add[i][1])
abs_cen = start_dist.dot(F_updated.sum(axis = 1))[0,0]
F = F_updated
ac_scores.append(abs_cen)
i += 1
return links_to_add, ac_scores