我正在尝试根据边缘的权重对网络中的节点进行可视化聚类：具有高边缘权重的节点应该在输出图像的 2d 欧几里得空间中靠近。
我用 NetworkX 构建了我的网络并将其转换为PyVis以显示它。我想知道是否有一种方法可以设置 PyVis，以便它们之间具有高权重边缘的节点靠近，理想情况下，我希望它看起来像这个例子。
我还想知道我是否应该先发制人地将 NetworkX 中的节点集群，标记它们，然后使用此信息在附近显示它们（集群参见此答案）。
另一种选择是删除所有边缘，除了那些具有高相对值的边缘（0.0075在下面的示例中），但我不知道这是否有助于让 PyVis 将节点可视地聚集在一起。
或者是否有任何其他选择来获得一些不错的东西（PageRank 等）？似乎缺乏关于如何正确可视化图形数据的信息（除此之外我没有发现太多信息）。
这是一个 MWE 来重现我所看到的；基本上，在这个最小的例子中，我希望看到节点[7,9,16,51,57]聚集在一起，与其他节点相比（理想情况下，我希望它看起来像这个例子 - 我以后总能弄清楚颜色）：
def draw_graph3( networkx_graph, notebook=True, output_filename='graph.html', open_output=False,
show_buttons=False, only_physics_buttons=False ):
# import
from pyvis import network as net
# make a pyvis network
pyvis_graph = net.Network( notebook=notebook )
pyvis_graph.width = '1000px'
# for each node and its attributes in the networkx graph
for node, node_attrs in networkx_graph.nodes( data=True ):
pyvis_graph.add_node( node, **node_attrs )
# print(node,node_attrs)
# for each edge and its attributes in the networkx graph
for source, target, edge_attrs in networkx_graph.edges( data=True ):
# if value/width not specified directly, and weight is specified, set 'value' to 'weight'
if not 'value' in edge_attrs and not 'width' in edge_attrs and 'weight' in edge_attrs:
# place at key 'value' the weight of the edge
edge_attrs[ 'value' ] = edge_attrs[ 'weight' ]
# add the edge
pyvis_graph.add_edge( source, target, **edge_attrs )
# turn buttons on
if show_buttons:
if only_physics_buttons:
pyvis_graph.show_buttons( filter_=[ 'physics' ] )
else:
pyvis_graph.show_buttons()
# return and also save
pyvis_graph.show( output_filename )
if open_output:
import webbrowser
import os
webbrowser.open( f'file://{os.getcwd()}/{output_filename}' )
# Build NetworkX graph
import networkx as nx
pre_neurons = [7, 9, 16, 51, 57, 114, 122, 126, 129, 138, 170, 202, 208, 221, 254]
post_neurons = [7, 9, 16, 51, 57, 114, 122, 126, 129, 138, 170, 202, 208, 221, 254]
edges_pre = [7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16, 16, 16, 51, 51, 51, 51, 51, 51, 57, 57, 57, 57, 57, 57, 114, 114, 114, 114, 114, 114, 114, 114, 122, 122, 122, 126, 126, 126, 126, 126, 126, 129, 129, 129, 129, 129, 138, 138, 138, 138, 138, 138, 170, 170, 170, 170, 170, 170, 202, 202, 202, 202, 202, 202, 202, 208, 208, 208, 208, 208, 208, 221, 221, 254, 254, 254, 254, 254, 254, 254, 254]
edges_post = [122, 129, 170, 51, 122, 129, 138, 208, 7, 9, 51, 57, 114, 129, 170, 208, 221, 9, 129, 170, 202, 221, 254, 114, 129, 170, 202, 208, 254, 51, 122, 126, 129, 138, 202, 208, 254, 114, 202, 208, 9, 51, 122, 202, 221, 254, 51, 114, 126, 138, 170, 7, 57, 114, 126, 129, 170, 7, 9, 57, 126, 138, 221, 51, 114, 126, 129, 170, 208, 221, 7, 51, 138, 170, 202, 254, 51, 170, 9, 16, 57, 114, 122, 126, 138, 170]
edges_weights = [0.0, 0.0, 0.0, 0.0075, 0.0, 0.0, 0.0, 0.0, 0.0075, 0.0075, 0.0075, 0.0075, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0075, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0, 0.0, 0.0, 0.0005, 0.0005, 0.0005, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0, 0.0005, 0.0, 0.0, 0.0, 0.0005, 0.0005, 0.0005, 0.0005, 0.0005]
g = nx.Graph()
g.add_nodes_from( pre_neurons )
g.add_nodes_from( post_neurons )
for i, j, w in zip( edges_pre, edges_post, edges_weights ):
g.add_edge( i, j, weight=w )
a = draw_graph3( g, output_filename='test.html', open_output=True, notebook=False, show_buttons=True,
only_physics_buttons=True )