def plot_graph(args):
if not isinstance(args.dataset, list):
args.dataset = [args.dataset]
for name in args.dataset:
dataset = build_dataset_from_name(name)
data = dataset[0]
depth = args.depth
pic_file = osp.join(args.save_dir, f'display_{name}.png')
col_names = [
'Dataset', '#nodes', '#edges', '#features', '#classes',
'#labeled data'
]
tab_data = [[
name, data.x.shape[0], data.edge_index.shape[1], data.x.shape[1],
len(set(data.y.numpy())),
sum(data.train_mask.numpy())
]]
print(tabulate(tab_data, headers=col_names, tablefmt='psql'))
G = nx.Graph()
G.add_edges_from([
tuple(data.edge_index[:, i].numpy())
for i in range(data.edge_index.shape[1])
])
s = random.choice(list(G.nodes()))
q = [s]
node_set = set([s])
node_index = {s: 0}
max_index = 1
for _ in range(depth):
nq = []
for x in q:
for key in G[x].keys():
if key not in node_set:
nq.append(key)
node_set.add(key)
node_index[key] = node_index[x] + 1
if len(nq) > 0:
max_index += 1
q = nq
cmap = cm.rainbow(np.linspace(0.0, 1.0, max_index))
for node, index in node_index.items():
G.nodes[node]['color'] = cmap[index]
G.nodes[node]['size'] = (max_index - index) * 50
fig, ax = plt.subplots()
plot_network(G.subgraph(list(node_set)), node_style=use_attributes())
plt.savefig(pic_file)
print(f'Sampled ego network saved to {pic_file} .')
def _call(self, dataset="cora", seed=-1, depth=3, **kwargs):
if isinstance(dataset, list):
dataset = dataset[0]
name = dataset
dataset = build_dataset_from_name(name)
data = dataset[0]
G = nx.Graph()
edge_index = torch.stack(data.edge_index)
G.add_edges_from([
tuple(edge_index[:, i].numpy()) for i in range(edge_index.shape[1])
])
if seed == -1:
seed = random.choice(list(G.nodes()))
q = [seed]
node_set = set([seed])
node_index = {seed: 0}
max_index = 1
for _ in range(depth):
nq = []
for x in q:
for key in G[x].keys():
if key not in node_set:
nq.append(key)
node_set.add(key)
node_index[key] = node_index[x] + 1
if len(nq) > 0:
max_index += 1
q = nq
cmap = cm.rainbow(np.linspace(0.0, 1.0, max_index))
for node, index in node_index.items():
G.nodes[node]["color"] = cmap[index]
G.nodes[node]["size"] = (max_index - index) * 50
pic_file = f"{name}.png"
plt.subplots()
plot_network(G.subgraph(list(node_set)), node_style=use_attributes())
plt.savefig(pic_file)
print(f"Sampled ego network saved to {pic_file}")
return q
import matplotlib.pyplot as plt
from grave import plot_network, use_attributes
toy_network = nx.barbell_graph(10, 14)
toy_centrality = closeness_centrality(toy_network)
max_centrality = max(toy_centrality.values())
for u, v, edge_attributes in toy_network.edges.data():
c = (toy_centrality[u] + toy_centrality[v]) / 2
color_idx = (c / max_centrality)
cmap = plt.get_cmap()
edge_attributes['color'] = cmap(color_idx)
edge_attributes['width'] = 2
for node, node_attributes in toy_network.nodes.data():
node_attributes['size'] = (1 - (toy_centrality[node] / max_centrality) +
.1) * 100
def edge_style(edge_attributes):
return {'linewidth': edge_attributes.get('weight', 1)}
fig, ax = plt.subplots()
plot_network(toy_network,
layout='spring',
node_style=use_attributes(),
edge_style=use_attributes('color'))
plt.show()
"""
Grave Documentation
-------------------
"""
import networkx as nx
from networkx.algorithms.approximation.dominating_set import min_weighted_dominating_set
import matplotlib.pyplot as plt
from grave import plot_network, use_attributes
toy_network = nx.barbell_graph(10, 14)
dom_set = min_weighted_dominating_set(toy_network)
for node, node_attrs in toy_network.nodes(data=True):
if node in dom_set:
node_attrs['color'] = 'red'
else:
node_attrs['color'] = 'black'
node_attrs['size'] = 50
fig, ax = plt.subplots()
plot_network(toy_network, node_style=use_attributes())
plt.show()
# create attribute for label
nx.set_edge_attributes(H,
{e: G.edges[e]['weight'] for e in H.edges},
'label')
# create stylers
def transfer_G_layout(network):
return {n: G.position[n] for n in network}
def elabel_base_style(attr):
return {'font_size': 4,
'font_weight': 'bold',
'font_family': 'sans-serif',
'font_color': 'b',
'rotate': True, # TODO: make rotation less granular
}
elabel_style = grave.style_merger(grave.use_attributes('label'),
elabel_base_style)
grave.plot_network(H, transfer_G_layout,
node_style=dict(node_size=20),
edge_label_style=elabel_style,
node_label_style={'font_weight': 'bold'})
# scale the axes equally
plt.xlim(-5000, 500)
plt.ylim(-2000, 3500)
plt.show()
