在网络图中，将鼠标悬停在网络组件上时如何突出显示它们?

问题描述

我制作了一张网络图.现在我想添加3个功能-

I have made a network graph. Now I want to add 3 functionalities -

• 当悬停在一条边上时，它应突出显示与该边相连的节点

• when hovering over an edge, it should highlight the nodes connected with that edge

当悬停在节点上时，它应突出显示所有节点以及连接到该节点的边缘

when hovering over a node, it should highlight all the nodes and edges connected to that node

在网络中选择一个子区域时(通过拖动鼠标)，而不是放大图形，而是应突出显示该选定子区域中的节点和边

when selecting a sub-area in the network (by dragging the mouse), instead of zooming into the graph, it should highlight the nodes and edges in that selected sub-area

我该怎么做?我发现了这支笔，但是我不知道如何适应它Python以自​​动方式对图中的每个节点执行此操作.

How do I do these? I found this pen, but I don't know how to adapt it to Python do this in an automated manner for every node in the graph.

这是我的代码:

import plotly.graph_objs as go
import networkx as nx
from plotly.offline import download_plotlyjs, init_notebook_mode,  iplot, plot

init_notebook_mode(connected=True)

#create graph G
G = nx.karate_club_graph()

# adjust node size according to degree, etc
d = nx.degree(G)
node_sizes = []
for i in d:
    _, value = i
    node_sizes.append(3*value+5)

#get a x,y position for each node
pos = nx.circular_layout(G)

#add a pos attribute to each node
for node in G.nodes:
    G.nodes[node]['pos'] = list(pos[node])

pos=nx.get_node_attributes(G,'pos')


dmin=1
ncenter=0
for n in pos:
    x,y=pos[n]
    d=(x-0.5)**2+(y-0.5)**2
    if d<dmin:
        ncenter=n
        dmin=d

p=nx.single_source_shortest_path_length(G,ncenter)

#Create Edges
edge_trace = go.Scatter(
    x=[],
    y=[],
    line=dict(width=0.5,color='#888'),
    hoverinfo='none',
    mode='lines')

edge_trace['line'] = dict(width=0.5,color='#FF0000')

for edge in G.edges():
    x0, y0 = G.node[edge[0]]['pos']
    x1, y1 = G.node[edge[1]]['pos']
    edge_trace['x'] += tuple([x0, x1, None])
    edge_trace['y'] += tuple([y0, y1, None])

node_trace = go.Scatter(
    x=[],
    y=[],
    text=[],
    mode='markers',
    hoverinfo='text',
    marker=dict(
        showscale=True,
        # colorscale options
        #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |
        #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |
        #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |
        colorscale='Viridis',
        reversescale=True,
        color=[],
        size=node_sizes,
        colorbar=dict(
            thickness=15,
            title='Node Connections',
            xanchor='left',
            titleside='right'
        ),
        line=dict(width=2)))

for node in G.nodes():
    x, y = G.node[node]['pos']
    node_trace['x'] += tuple([x])
    node_trace['y'] += tuple([y])

#add color to node points
for node, adjacencies in enumerate(G.adjacency()):
    node_trace['marker']['color']+=tuple([len(adjacencies[1])])
    node_info = 'Name: ' + str(adjacencies[0]) + '<br># of connections: '+str(len(adjacencies[1]))
    node_trace['text']+=tuple([node_info])

f = go.Figure(data=[edge_trace, node_trace],
              layout=go.Layout(
                title='<br>Network Graph of Karate Club',
                titlefont=dict(size=16),
                showlegend=False,
                hovermode='closest',
                width=880,
                height=800,
                margin=dict(b=20,l=5,r=5,t=40),
                annotations=[ dict(
                    showarrow=False,
                    xref="paper", yref="paper",
                    x=0.005, y=-0.002 ) ],
                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
              )
             )

iplot(f)

推荐答案

您可以使用 bokeh 一个>库.它具有用于图形可视化的良好子模块. 此处是一些图示例.您的问题可以通过以下代码解决:

You can use bokeh library. It has good submodules for graph visualization. Here are some graph viz examples. Your problem can be solved with this code:

import networkx as nx

from bokeh.io import show, output_file
from bokeh.models import Plot, Range1d, MultiLine, Circle
from bokeh.models import HoverTool, TapTool, BoxSelectTool
from bokeh.models.graphs import from_networkx
from bokeh.models.graphs import NodesAndLinkedEdges, EdgesAndLinkedNodes
from bokeh.palettes import Spectral4

G = nx.gnp_random_graph(20, 0.1)

plot = Plot(
    plot_width=400,
    plot_height=400,
    x_range=Range1d(-1.1,1.1),
    y_range=Range1d(-1.1,1.1)
)
plot.title.text = "WAKA"

plot.add_tools(
    HoverTool(tooltips=None),
    TapTool(),
    BoxSelectTool()
)

graph_renderer = from_networkx(
    G,
    nx.circular_layout,
    scale=1,
    center=(0,0)
)

graph_renderer.node_renderer.glyph = Circle(
    size=15,
    fill_color=Spectral4[0]
)
graph_renderer.node_renderer.selection_glyph = Circle(
    size=25,
    fill_color=Spectral4[2]
)
graph_renderer.node_renderer.hover_glyph = Circle(
    size=20,
    fill_color=Spectral4[1]
)

graph_renderer.edge_renderer.glyph = MultiLine(
    line_color="#CCCCCC",
    line_alpha=0.8,
    line_width=3
)
graph_renderer.edge_renderer.selection_glyph = MultiLine(
    line_color=Spectral4[2],
    line_width=7
)
graph_renderer.edge_renderer.hover_glyph = MultiLine(
    line_color=Spectral4[1],
    line_width=5
)

graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()

plot.renderers.append(graph_renderer)

output_file("waka.html")
show(plot)

这篇关于在网络图中，将鼠标悬停在网络组件上时如何突出显示它们?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！