def greedy_modularity_communities(G, weight=None, resolution=1):
"""Find communities in G using greedy modularity maximization.
This function uses Clauset-Newman-Moore greedy modularity maximization [2]_.
This method currently supports the Graph class.
Greedy modularity maximization begins with each node in its own community
and joins the pair of communities that most increases modularity until no
such pair exists.
This function maximizes the generalized modularity, where `resolution`
is the resolution parameter, often expressed as $\gamma$.
See :func:`~networkx.algorithms.community.quality.modularity`.
Parameters
----------
G : NetworkX graph
weight : string or None, optional (default=None)
The name of an edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
Returns
-------
list
A list of sets of nodes, one for each community.
Sorted by length with largest communities first.
Examples
--------
>>> from networkx.algorithms.community import greedy_modularity_communities
>>> G = nx.karate_club_graph()
>>> c = list(greedy_modularity_communities(G))
>>> sorted(c[0])
[8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]
See Also
--------
modularity
References
----------
.. [1] M. E. J Newman "Networks: An Introduction", page 224
Oxford University Press 2011.
.. [2] Clauset, A., Newman, M. E., & Moore, C.
"Finding community structure in very large networks."
Physical Review E 70(6), 2004.
.. [3] Reichardt and Bornholdt "Statistical Mechanics of Community
Detection" Phys. Rev. E74, 2006.
"""
# Count nodes and edges
N = len(G.nodes())
m = sum([d.get("weight", 1) for u, v, d in G.edges(data=True)])
q0 = 1.0 / (2.0 * m)
# Map node labels to contiguous integers
label_for_node = {i: v for i, v in enumerate(G.nodes())}
node_for_label = {label_for_node[i]: i for i in range(N)}
# Calculate degrees
k_for_label = G.degree(G.nodes(), weight=weight)
k = [k_for_label[label_for_node[i]] for i in range(N)]
# Initialize community and merge lists
communities = {i: frozenset([i]) for i in range(N)}
merges = []
# Initial modularity
partition = [[label_for_node[x] for x in c] for c in communities.values()]
q_cnm = modularity(G, partition, resolution=resolution)
# Initialize data structures
# CNM Eq 8-9 (Eq 8 was missing a factor of 2 (from A_ij + A_ji)
# a[i]: fraction of edges within community i
# dq_dict[i][j]: dQ for merging community i, j
# dq_heap[i][n] : (-dq, i, j) for communitiy i nth largest dQ
# H[n]: (-dq, i, j) for community with nth largest max_j(dQ_ij)
a = [k[i] * q0 for i in range(N)]
dq_dict = {
i: {
j: 2 * q0 * G.get_edge_data(i, j).get(weight, 1.0) -
2 * resolution * k[i] * k[j] * q0 * q0
for j in
[node_for_label[u] for u in G.neighbors(label_for_node[i])]
if j != i
}
for i in range(N)
}
dq_heap = [
MappedQueue([(-dq, i, j) for j, dq in dq_dict[i].items()])
for i in range(N)
]
H = MappedQueue([dq_heap[i].h[0] for i in range(N) if len(dq_heap[i]) > 0])
# Merge communities until we can't improve modularity
while len(H) > 1:
# Find best merge
# Remove from heap of row maxes
# Ties will be broken by choosing the pair with lowest min community id
try:
dq, i, j = H.pop()
except IndexError:
break
dq = -dq
# Remove best merge from row i heap
dq_heap[i].pop()
# Push new row max onto H
if len(dq_heap[i]) > 0:
H.push(dq_heap[i].h[0])
# If this element was also at the root of row j, we need to remove the
# duplicate entry from H
if dq_heap[j].h[0] == (-dq, j, i):
H.remove((-dq, j, i))
# Remove best merge from row j heap
dq_heap[j].remove((-dq, j, i))
# Push new row max onto H
if len(dq_heap[j]) > 0:
H.push(dq_heap[j].h[0])
else:
# Duplicate wasn't in H, just remove from row j heap
dq_heap[j].remove((-dq, j, i))
# Stop when change is non-positive
if dq <= 0:
break
# Perform merge
communities[j] = frozenset(communities[i] | communities[j])
del communities[i]
merges.append((i, j, dq))
# New modularity
q_cnm += dq
# Get list of communities connected to merged communities
i_set = set(dq_dict[i].keys())
j_set = set(dq_dict[j].keys())
all_set = (i_set | j_set) - {i, j}
both_set = i_set & j_set
# Merge i into j and update dQ
for k in all_set:
# Calculate new dq value
if k in both_set:
dq_jk = dq_dict[j][k] + dq_dict[i][k]
elif k in j_set:
dq_jk = dq_dict[j][k] - 2.0 * resolution * a[i] * a[k]
else:
# k in i_set
dq_jk = dq_dict[i][k] - 2.0 * resolution * a[j] * a[k]
# Update rows j and k
for row, col in [(j, k), (k, j)]:
# Save old value for finding heap index
if k in j_set:
d_old = (-dq_dict[row][col], row, col)
else:
d_old = None
# Update dict for j,k only (i is removed below)
dq_dict[row][col] = dq_jk
# Save old max of per-row heap
if len(dq_heap[row]) > 0:
d_oldmax = dq_heap[row].h[0]
else:
d_oldmax = None
# Add/update heaps
d = (-dq_jk, row, col)
if d_old is None:
# We're creating a new nonzero element, add to heap
dq_heap[row].push(d)
else:
# Update existing element in per-row heap
dq_heap[row].update(d_old, d)
# Update heap of row maxes if necessary
if d_oldmax is None:
# No entries previously in this row, push new max
H.push(d)
else:
# We've updated an entry in this row, has the max changed?
if dq_heap[row].h[0] != d_oldmax:
H.update(d_oldmax, dq_heap[row].h[0])
# Remove row/col i from matrix
i_neighbors = dq_dict[i].keys()
for k in i_neighbors:
# Remove from dict
dq_old = dq_dict[k][i]
del dq_dict[k][i]
# Remove from heaps if we haven't already
if k != j:
# Remove both row and column
for row, col in [(k, i), (i, k)]:
# Check if replaced dq is row max
d_old = (-dq_old, row, col)
if dq_heap[row].h[0] == d_old:
# Update per-row heap and heap of row maxes
dq_heap[row].remove(d_old)
H.remove(d_old)
# Update row max
if len(dq_heap[row]) > 0:
H.push(dq_heap[row].h[0])
else:
# Only update per-row heap
dq_heap[row].remove(d_old)
del dq_dict[i]
# Mark row i as deleted, but keep placeholder
dq_heap[i] = MappedQueue()
# Merge i into j and update a
a[j] += a[i]
a[i] = 0
communities = [
frozenset([label_for_node[i] for i in c])
for c in communities.values()
]
return sorted(communities, key=len, reverse=True)
def greedy_modularity_communities(G,
weight=None,
resolution=1,
n_communities=1):
r"""Find communities in G using greedy modularity maximization.
This function uses Clauset-Newman-Moore greedy modularity maximization [2]_.
Greedy modularity maximization begins with each node in its own community
and joins the pair of communities that most increases modularity until no
such pair exists or until number of communities `n_communities` is reached.
This function maximizes the generalized modularity, where `resolution`
is the resolution parameter, often expressed as $\gamma$.
See :func:`~networkx.algorithms.community.quality.modularity`.
Parameters
----------
G : NetworkX graph
weight : string or None, optional (default=None)
The name of an edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
resolution : float (default=1)
If resolution is less than 1, modularity favors larger communities.
Greater than 1 favors smaller communities.
n_communities: int
Desired number of communities: the community merging process is
terminated once this number of communities is reached, or until
modularity can not be further increased. Must be between 1 and the
total number of nodes in `G`. Default is ``1``, meaning the community
merging process continues until all nodes are in the same community
or until the best community structure is found.
Returns
-------
partition: list
A list of frozensets of nodes, one for each community.
Sorted by length with largest communities first.
Examples
--------
>>> from networkx.algorithms.community import greedy_modularity_communities
>>> G = nx.karate_club_graph()
>>> c = greedy_modularity_communities(G)
>>> sorted(c[0])
[8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]
See Also
--------
modularity
References
----------
.. [1] Newman, M. E. J. "Networks: An Introduction", page 224
Oxford University Press 2011.
.. [2] Clauset, A., Newman, M. E., & Moore, C.
"Finding community structure in very large networks."
Physical Review E 70(6), 2004.
.. [3] Reichardt and Bornholdt "Statistical Mechanics of Community
Detection" Phys. Rev. E74, 2006.
.. [4] Newman, M. E. J."Analysis of weighted networks"
Physical Review E 70(5 Pt 2):056131, 2004.
"""
directed = G.is_directed()
N = G.number_of_nodes()
if (n_communities < 1) or (n_communities > N):
raise ValueError(
f"n_communities must be between 1 and {N}. Got {n_communities}")
# Count edges (or the sum of edge-weights for weighted graphs)
m = G.size(weight)
q0 = 1 / m
# Calculate degrees (notation from the papers)
# a : the fraction of (weighted) out-degree for each node
# b : the fraction of (weighted) in-degree for each node
if directed:
a = {
node: deg_out * q0
for node, deg_out in G.out_degree(weight=weight)
}
b = {node: deg_in * q0 for node, deg_in in G.in_degree(weight=weight)}
else:
a = b = {node: deg * q0 * 0.5 for node, deg in G.degree(weight=weight)}
# this preliminary step collects the edge weights for each node pair
# It handles multigraph and digraph and works fine for graph.
dq_dict = defaultdict(lambda: defaultdict(float))
for u, v, wt in G.edges(data=weight, default=1):
if u == v:
continue
dq_dict[u][v] += wt
dq_dict[v][u] += wt
# now scale and subtract the expected edge-weights term
for u, nbrdict in dq_dict.items():
for v, wt in nbrdict.items():
dq_dict[u][v] = q0 * wt - resolution * (a[u] * b[v] + b[u] * a[v])
# Use -dq to get a max_heap instead of a min_heap
# dq_heap holds a heap for each node's neighbors
dq_heap = {
u: MappedQueue({(u, v): -dq
for v, dq in dq_dict[u].items()})
for u in G
}
# H -> all_dq_heap holds a heap with the best items for each node
H = MappedQueue([dq_heap[n].heap[0] for n in G if len(dq_heap[n]) > 0])
# Initialize single-node communities
communities = {n: frozenset([n]) for n in G}
# Merge communities until we can't improve modularity or until desired number of
# communities (n_communities) is reached.
while len(H) > n_communities:
# Find best merge
# Remove from heap of row maxes
# Ties will be broken by choosing the pair with lowest min community id
try:
negdq, u, v = H.pop()
except IndexError:
break
dq = -negdq
# Remove best merge from row u heap
dq_heap[u].pop()
# Push new row max onto H
if len(dq_heap[u]) > 0:
H.push(dq_heap[u].heap[0])
# If this element was also at the root of row v, we need to remove the
# duplicate entry from H
if dq_heap[v].heap[0] == (v, u):
H.remove((v, u))
# Remove best merge from row v heap
dq_heap[v].remove((v, u))
# Push new row max onto H
if len(dq_heap[v]) > 0:
H.push(dq_heap[v].heap[0])
else:
# Duplicate wasn't in H, just remove from row v heap
dq_heap[v].remove((v, u))
# Stop when change is non-positive (no improvement possible)
if dq <= 0:
break
# Perform merge
communities[v] = frozenset(communities[u] | communities[v])
del communities[u]
# Get neighbor communities connected to the merged communities
u_nbrs = set(dq_dict[u])
v_nbrs = set(dq_dict[v])
all_nbrs = (u_nbrs | v_nbrs) - {u, v}
both_nbrs = u_nbrs & v_nbrs
# Update dq for merge of u into v
for w in all_nbrs:
# Calculate new dq value
if w in both_nbrs:
dq_vw = dq_dict[v][w] + dq_dict[u][w]
elif w in v_nbrs:
dq_vw = dq_dict[v][w] - resolution * (a[u] * b[w] +
a[w] * b[u])
else: # w in u_nbrs
dq_vw = dq_dict[u][w] - resolution * (a[v] * b[w] +
a[w] * b[v])
# Update rows v and w
for row, col in [(v, w), (w, v)]:
dq_heap_row = dq_heap[row]
# Update dict for v,w only (u is removed below)
dq_dict[row][col] = dq_vw
# Save old max of per-row heap
if len(dq_heap_row) > 0:
d_oldmax = dq_heap_row.heap[0]
else:
d_oldmax = None
# Add/update heaps
d = (row, col)
d_negdq = -dq_vw
# Save old value for finding heap index
if w in v_nbrs:
# Update existing element in per-row heap
dq_heap_row.update(d, d, priority=d_negdq)
else:
# We're creating a new nonzero element, add to heap
dq_heap_row.push(d, priority=d_negdq)
# Update heap of row maxes if necessary
if d_oldmax is None:
# No entries previously in this row, push new max
H.push(d, priority=d_negdq)
else:
# We've updated an entry in this row, has the max changed?
row_max = dq_heap_row.heap[0]
if d_oldmax != row_max or d_oldmax.priority != row_max.priority:
H.update(d_oldmax, row_max)
# Remove row/col u from dq_dict matrix
for w in dq_dict[u]:
# Remove from dict
dq_old = dq_dict[w][u]
del dq_dict[w][u]
# Remove from heaps if we haven't already
if w != v:
# Remove both row and column
for row, col in [(w, u), (u, w)]:
dq_heap_row = dq_heap[row]
# Check if replaced dq is row max
d_old = (row, col)
if dq_heap_row.heap[0] == d_old:
# Update per-row heap and heap of row maxes
dq_heap_row.remove(d_old)
H.remove(d_old)
# Update row max
if len(dq_heap_row) > 0:
H.push(dq_heap_row.heap[0])
else:
# Only update per-row heap
dq_heap_row.remove(d_old)
del dq_dict[u]
# Mark row u as deleted, but keep placeholder
dq_heap[u] = MappedQueue()
# Merge u into v and update a
a[v] += a[u]
a[u] = 0
if directed:
b[v] += b[u]
b[u] = 0
return sorted(communities.values(), key=len, reverse=True)
def greedy_modularity_communities(G, K, weight=None):
"""Find communities in graph using Clauset-Newman-Moore greedy modularity
maximization. This method currently supports the Graph class and does not
consider edge weights.
Greedy modularity maximization begins with each node in its own community
and joins the pair of communities that most increases modularity until no
such pair exists.
Parameters
----------
G : NetworkX graph
Returns
-------
Yields sets of nodes, one for each community.
Examples
--------
>>> from networkx.algorithms.community import greedy_modularity_communities
>>> G = nx.karate_club_graph()
>>> c = list(greedy_modularity_communities(G))
>>> sorted(c[0])
[8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]
References
----------
.. [1] M. E. J Newman 'Networks: An Introduction', page 224
Oxford University Press 2011.
.. [2] Clauset, A., Newman, M. E., & Moore, C.
"Finding community structure in very large networks."
Physical Review E 70(6), 2004.
"""
G = nx.from_numpy_array(G.detach().numpy(), create_using=nx.DiGraph())
# Count nodes and edges
N = len(G.nodes())
m = sum([d.get('weight', 1) for u, v, d in G.edges(data=True)])
q0 = 1.0 / (2.0 * m)
# Map node labels to contiguous integers
label_for_node = dict((i, v) for i, v in enumerate(G.nodes()))
node_for_label = dict((label_for_node[i], i) for i in range(N))
# Calculate degrees
k_for_label = G.degree(G.nodes(), weight=weight)
k = [k_for_label[label_for_node[i]] for i in range(N)]
# Initialize community and merge lists
communities = dict((i, frozenset([i])) for i in range(N))
merges = []
# Initial modularity
partition = [[label_for_node[x] for x in c] for c in communities.values()]
q_cnm = modularity(G, partition)
# Initialize data structures
# CNM Eq 8-9 (Eq 8 was missing a factor of 2 (from A_ij + A_ji)
# a[i]: fraction of edges within community i
# dq_dict[i][j]: dQ for merging community i, j
# dq_heap[i][n] : (-dq, i, j) for communitiy i nth largest dQ
# H[n]: (-dq, i, j) for community with nth largest max_j(dQ_ij)
a = [k[i] * q0 for i in range(N)]
dq_dict = dict(
(i,
dict((j, q0 * (G[i][j]['weight'] + G[j][i]['weight']) -
2 * k[i] * k[j] * q0 * q0) for j in
[node_for_label[u] for u in G.neighbors(label_for_node[i])]
if j != i)) for i in range(N))
# print(min([len(x[1]) for x in dq_dict.values()]))
# raise Exception()
# return dq_dict
dq_heap = [
MappedQueue([(-dq, i, j) for j, dq in dq_dict[i].items()])
for i in range(N)
]
H = MappedQueue([dq_heap[i].h[0] for i in range(N) if len(dq_heap[i]) > 0])
# Merge communities until we can't improve modularity
while len(H) > 1:
# Find best merge
# Remove from heap of row maxes
# Ties will be broken by choosing the pair with lowest min community id
try:
dq, i, j = H.pop()
except IndexError:
break
dq = -dq
# Remove best merge from row i heap
dq_heap[i].pop()
# Push new row max onto H
if len(dq_heap[i]) > 0:
H.push(dq_heap[i].h[0])
# If this element was also at the root of row j, we need to remove the
# duplicate entry from H
if dq_heap[j].h[0] == (-dq, j, i):
H.remove((-dq, j, i))
# Remove best merge from row j heap
dq_heap[j].remove((-dq, j, i))
# Push new row max onto H
if len(dq_heap[j]) > 0:
H.push(dq_heap[j].h[0])
else:
# Duplicate wasn't in H, just remove from row j heap
dq_heap[j].remove((-dq, j, i))
# Perform merge
communities[j] = frozenset(communities[i] | communities[j])
del communities[i]
merges.append((i, j, dq))
# print(len(communities))
# Stop when change is non-positive
if len(communities) == K:
break
# New modularity
q_cnm += dq
# Get list of communities connected to merged communities
i_set = set(dq_dict[i].keys())
j_set = set(dq_dict[j].keys())
all_set = (i_set | j_set) - set([i, j])
both_set = i_set & j_set
# Merge i into j and update dQ
for k in all_set:
# Calculate new dq value
if k in both_set:
dq_jk = dq_dict[j][k] + dq_dict[i][k]
elif k in j_set:
dq_jk = dq_dict[j][k] - 2.0 * a[i] * a[k]
else:
# k in i_set
dq_jk = dq_dict[i][k] - 2.0 * a[j] * a[k]
# Update rows j and k
for row, col in [(j, k), (k, j)]:
# Save old value for finding heap index
if k in j_set:
d_old = (-dq_dict[row][col], row, col)
else:
d_old = None
# Update dict for j,k only (i is removed below)
dq_dict[row][col] = dq_jk
# Save old max of per-row heap
if len(dq_heap[row]) > 0:
d_oldmax = dq_heap[row].h[0]
else:
d_oldmax = None
# Add/update heaps
d = (-dq_jk, row, col)
if d_old is None:
# We're creating a new nonzero element, add to heap
dq_heap[row].push(d)
else:
# Update existing element in per-row heap
dq_heap[row].update(d_old, d)
# Update heap of row maxes if necessary
if d_oldmax is None:
# No entries previously in this row, push new max
H.push(d)
else:
# We've updated an entry in this row, has the max changed?
if dq_heap[row].h[0] != d_oldmax:
H.update(d_oldmax, dq_heap[row].h[0])
# Remove row/col i from matrix
i_neighbors = dq_dict[i].keys()
for k in i_neighbors:
# Remove from dict
dq_old = dq_dict[k][i]
del dq_dict[k][i]
# Remove from heaps if we haven't already
if k != j:
# Remove both row and column
for row, col in [(k, i), (i, k)]:
# Check if replaced dq is row max
d_old = (-dq_old, row, col)
if dq_heap[row].h[0] == d_old:
# Update per-row heap and heap of row maxes
dq_heap[row].remove(d_old)
H.remove(d_old)
# Update row max
if len(dq_heap[row]) > 0:
H.push(dq_heap[row].h[0])
else:
# Only update per-row heap
# if d_old in dq_heap[row].d:
dq_heap[row].remove(d_old)
del dq_dict[i]
# Mark row i as deleted, but keep placeholder
dq_heap[i] = MappedQueue()
# Merge i into j and update a
a[j] += a[i]
a[i] = 0
# communities = [
# set([label_for_node[i] for i in c])
# for c in communities.values()]
heap = []
for j in communities:
heapq.heappush(heap, (a[j], set(communities[j])))
while len(heap) > K:
weight1, com1 = heapq.heappop(heap)
weight2, com2 = heapq.heappop(heap)
com1.update(com2)
heapq.heappush(heap, (weight1 + weight2, com1))
communities = [x[1] for x in heap]
r = torch.zeros(N, K)
print(len(communities))
for i, c in enumerate(communities):
for v in c:
r[v, i] = 1
return r
def greedy_modularity_communities(G, weight=None):
"""Find communities in graph using Clauset-Newman-Moore greedy modularity
maximization. This method currently supports the Graph class and does not
consider edge weights.
Greedy modularity maximization begins with each node in its own community
and joins the pair of communities that most increases modularity until no
such pair exists.
Parameters
----------
G : NetworkX graph
Returns
-------
Yields sets of nodes, one for each community.
Examples
--------
>>> from networkx.algorithms.community import greedy_modularity_communities
>>> G = nx.karate_club_graph()
>>> c = list(greedy_modularity_communities(G))
>>> sorted(c[0])
[8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]
References
----------
.. [1] M. E. J Newman 'Networks: An Introduction', page 224
Oxford University Press 2011.
.. [2] Clauset, A., Newman, M. E., & Moore, C.
"Finding community structure in very large networks."
Physical Review E 70(6), 2004.
"""
# Count nodes and edges
N = len(G.nodes())
m = sum([d.get('weight', 1) for u, v, d in G.edges(data=True)])
q0 = 1.0 / (2.0*m)
# Map node labels to contiguous integers
label_for_node = dict((i, v) for i, v in enumerate(G.nodes()))
node_for_label = dict((label_for_node[i], i) for i in range(N))
# Calculate degrees
k_for_label = G.degree(G.nodes(), weight=weight)
k = [k_for_label[label_for_node[i]] for i in range(N)]
# Initialize community and merge lists
communities = dict((i, frozenset([i])) for i in range(N))
merges = []
# Initial modularity
partition = [[label_for_node[x] for x in c] for c in communities.values()]
q_cnm = modularity(G, partition)
# Initialize data structures
# CNM Eq 8-9 (Eq 8 was missing a factor of 2 (from A_ij + A_ji)
# a[i]: fraction of edges within community i
# dq_dict[i][j]: dQ for merging community i, j
# dq_heap[i][n] : (-dq, i, j) for communitiy i nth largest dQ
# H[n]: (-dq, i, j) for community with nth largest max_j(dQ_ij)
a = [k[i]*q0 for i in range(N)]
dq_dict = dict(
(i, dict(
(j, 2*q0 - 2*k[i]*k[j]*q0*q0)
for j in [
node_for_label[u]
for u in G.neighbors(label_for_node[i])]
if j != i))
for i in range(N))
dq_heap = [
MappedQueue([
(-dq, i, j)
for j, dq in dq_dict[i].items()])
for i in range(N)]
H = MappedQueue([
dq_heap[i].h[0]
for i in range(N)
if len(dq_heap[i]) > 0])
# Merge communities until we can't improve modularity
while len(H) > 1:
# Find best merge
# Remove from heap of row maxes
# Ties will be broken by choosing the pair with lowest min community id
try:
dq, i, j = H.pop()
except IndexError:
break
dq = -dq
# Remove best merge from row i heap
dq_heap[i].pop()
# Push new row max onto H
if len(dq_heap[i]) > 0:
H.push(dq_heap[i].h[0])
# If this element was also at the root of row j, we need to remove the
# duplicate entry from H
if dq_heap[j].h[0] == (-dq, j, i):
H.remove((-dq, j, i))
# Remove best merge from row j heap
dq_heap[j].remove((-dq, j, i))
# Push new row max onto H
if len(dq_heap[j]) > 0:
H.push(dq_heap[j].h[0])
else:
# Duplicate wasn't in H, just remove from row j heap
dq_heap[j].remove((-dq, j, i))
# Stop when change is non-positive
if dq <= 0:
break
# Perform merge
communities[j] = frozenset(communities[i] | communities[j])
del communities[i]
merges.append((i, j, dq))
# New modularity
q_cnm += dq
# Get list of communities connected to merged communities
i_set = set(dq_dict[i].keys())
j_set = set(dq_dict[j].keys())
all_set = (i_set | j_set) - set([i, j])
both_set = i_set & j_set
# Merge i into j and update dQ
for k in all_set:
# Calculate new dq value
if k in both_set:
dq_jk = dq_dict[j][k] + dq_dict[i][k]
elif k in j_set:
dq_jk = dq_dict[j][k] - 2.0*a[i]*a[k]
else:
# k in i_set
dq_jk = dq_dict[i][k] - 2.0*a[j]*a[k]
# Update rows j and k
for row, col in [(j, k), (k, j)]:
# Save old value for finding heap index
if k in j_set:
d_old = (-dq_dict[row][col], row, col)
else:
d_old = None
# Update dict for j,k only (i is removed below)
dq_dict[row][col] = dq_jk
# Save old max of per-row heap
if len(dq_heap[row]) > 0:
d_oldmax = dq_heap[row].h[0]
else:
d_oldmax = None
# Add/update heaps
d = (-dq_jk, row, col)
if d_old is None:
# We're creating a new nonzero element, add to heap
dq_heap[row].push(d)
else:
# Update existing element in per-row heap
dq_heap[row].update(d_old, d)
# Update heap of row maxes if necessary
if d_oldmax is None:
# No entries previously in this row, push new max
H.push(d)
else:
# We've updated an entry in this row, has the max changed?
if dq_heap[row].h[0] != d_oldmax:
H.update(d_oldmax, dq_heap[row].h[0])
# Remove row/col i from matrix
i_neighbors = dq_dict[i].keys()
for k in i_neighbors:
# Remove from dict
dq_old = dq_dict[k][i]
del dq_dict[k][i]
# Remove from heaps if we haven't already
if k != j:
# Remove both row and column
for row, col in [(k, i), (i, k)]:
# Check if replaced dq is row max
d_old = (-dq_old, row, col)
if dq_heap[row].h[0] == d_old:
# Update per-row heap and heap of row maxes
dq_heap[row].remove(d_old)
H.remove(d_old)
# Update row max
if len(dq_heap[row]) > 0:
H.push(dq_heap[row].h[0])
else:
# Only update per-row heap
dq_heap[row].remove(d_old)
del dq_dict[i]
# Mark row i as deleted, but keep placeholder
dq_heap[i] = MappedQueue()
# Merge i into j and update a
a[j] += a[i]
a[i] = 0
communities = [
frozenset([label_for_node[i] for i in c])
for c in communities.values()]
return sorted(communities, key=len, reverse=True)
def greedy_modularity_communities(G, K):
# Code modified from https://networkx.github.io/documentation/latest/_modules/networkx/algorithms/community/modularity_max.html#greedy_modularity_communities
G = nx.from_numpy_array(G.detach().numpy(), nx.Graph())
node_num = len(G.nodes())
m = sum([d.get('weight', 1) for u, v, d in G.edges(data=True)])
q0 = 1.0 / (2.0 * m)
label_for_node = {}
for i, v in enumerate(G.nodes()):
label_for_node[i] = v
node_for_label = {}
communities = {}
k = []
a = []
merges = []
degree_for_label = G.degree(G.nodes())
for i in range(node_num):
node_for_label[label_for_node[i]] = i
communities[i] = frozenset([i])
k.append(degree_for_label[label_for_node[i]])
a.append(q0 * k[i])
partition = [[label_for_node[x] for x in c] for c in communities.values()]
q_cnm = modularity(G, partition)
dq_dict = dict(
(i,
dict((j, q0 * (G[i][j]['weight'] + G[j][i]['weight']) -
2 * k[i] * k[j] * q0 * q0) for j in
[node_for_label[u] for u in G.neighbors(label_for_node[i])]
if j != i)) for i in range(node_num))
dq_heap = [
MappedQueue([(-dq, i, j) for j, dq in dq_dict[i].items()])
for i in range(node_num)
]
H = MappedQueue(
[dq_heap[i].h[0] for i in range(node_num) if len(dq_heap[i]) > 0])
# Merge communities until we can't improve modularity
while len(H) > 1:
dq, i, j = H.pop()
dq = -dq
dq_heap[i].pop()
if len(dq_heap[i]) != 0:
H.push(dq_heap[i].h[0])
if dq_heap[j].h[0] == (-dq, j, i):
H.remove((-dq, j, i))
dq_heap[j].remove((-dq, j, i))
if len(dq_heap[j]) > 0:
H.push(dq_heap[j].h[0])
else:
dq_heap[j].remove((-dq, j, i))
communities[j] = frozenset(communities[i] | communities[j])
del communities[i]
merges.append((i, j, dq))
if len(communities) == K:
break
q_cnm += dq
i_set = set(dq_dict[i].keys())
j_set = set(dq_dict[j].keys())
all_set = (i_set | j_set) - set([i, j])
both_set = i_set & j_set
for k in all_set:
if k in both_set:
dq_jk = dq_dict[j][k] + dq_dict[i][k]
elif k in j_set:
dq_jk = dq_dict[j][k] - 2.0 * a[i] * a[k]
else:
dq_jk = dq_dict[i][k] - 2.0 * a[j] * a[k]
for row, col in [(j, k), (k, j)]:
if k in j_set:
d_old = (-dq_dict[row][col], row, col)
else:
d_old = None
dq_dict[row][col] = dq_jk
if len(dq_heap[row]) > 0:
d_oldmax = dq_heap[row].h[0]
else:
d_oldmax = None
d = (-dq_jk, row, col)
if d_old is None:
dq_heap[row].push(d)
else:
dq_heap[row].update(d_old, d)
if d_oldmax is None:
H.push(d)
else:
if dq_heap[row].h[0] != d_oldmax:
H.update(d_oldmax, dq_heap[row].h[0])
i_neighbors = dq_dict[i].keys()
for k in i_neighbors:
dq_old = dq_dict[k][i]
del dq_dict[k][i]
if k != j:
for row, col in [(k, i), (i, k)]:
d_old = (-dq_old, row, col)
if dq_heap[row].h[0] == d_old:
dq_heap[row].remove(d_old)
H.remove(d_old)
if len(dq_heap[row]) > 0:
H.push(dq_heap[row].h[0])
else:
dq_heap[row].remove(d_old)
del dq_dict[i]
dq_heap[i] = MappedQueue()
a[j] += a[i]
a[i] = 0
heap = []
for j in communities:
heapq.heappush(heap, (a[j], set(communities[j])))
while len(heap) > K:
weight1, com1 = heapq.heappop(heap)
weight2, com2 = heapq.heappop(heap)
com1.update(com2)
heapq.heappush(heap, (weight1 + weight2, com1))
communities = [x[1] for x in heap]
r = torch.zeros(node_num, K)
for i, c in enumerate(communities):
for v in c:
r[v, i] = 1
return r