在某些应用中，网络结构是动态变化的，例如社交网络中的人际关系随时间变化。我们可以使用Plotly来创建动态网络图，展示网络随时间的演变。

1. 创建动态网络数据

我们可以模拟一个简单的动态网络，其中节点和边在不同的时间步长中添加或删除。

import pandas as pd

# 创建时间步长的网络数据

time_steps = [

{ 'time': 1, 'edges': [(1, 2), (2, 3)]},

{ 'time': 2, 'edges': [(1, 2), (2, 3), (3, 4)]},

{ 'time': 3, 'edges': [(1, 2), (2, 3), (3, 4), (4, 5)]}

]

# 将网络数据转换为DataFrame

df = pd.DataFrame(time_steps)

2. 动态网络的可视化

我们可以使用Plotly的动画功能来展示网络的动态变化。

import plotly.graph_objects as go

# 创建初始的节点和边

G = nx.Graph()

G.add_edges_from(time_steps[0]['edges'])

pos = nx.spring_layout(G)

# 创建初始的边trace

edge_trace = go.Scatter(

x=[],

y=[],

line=dict(width=0.5, color='#888'),code>

hoverinfo='none',code>

mode='lines')code>

for edge in G.edges():

x0, y0 = pos[edge[0]]

x1, y1 = pos[edge[1]]

edge_trace['x'] += [x0, x1, None]

edge_trace['y'] += [y0, y1, None]

# 创建初始的节点trace

node_trace = go.Scatter(

x=[],

y=[],

text=[],

mode='markers+text',code>

textposition="top center",code>

hoverinfo='text',code>

marker=dict(

showscale=True,

colorscale='YlGnBu',code>

size=10,

colorbar=dict(

thickness=15,

title='Node Connections',code>

xanchor='left',code>

titleside='right'code>

),

line_width=2))

for node in G.nodes():

x, y = pos[node]

node_trace['x'] += [x]

node_trace['y'] += [y]

node_trace['text'] += [str(node)]

frames = []

# 创建每个时间步长的帧

for step in time_steps:

G.clear()

G.add_edges_from(step['edges'])

pos = nx.spring_layout(G)

edge_trace = go.Scatter(

x=[],

y=[],

line=dict(width=0.5, color='#888'),code>

hoverinfo='none',code>

mode='lines')code>

for edge in G.edges():

x0, y0 = pos[edge[0]]

x1, y1 = pos[edge[1]]

edge_trace['x'] += [x0, x1, None]

edge_trace['y'] += [y0, y1, None]

node_trace = go.Scatter(

x=[],

y=[],

text=[],

mode='markers+text',code>

textposition="top center",code>

hoverinfo='text',code>

marker=dict(

showscale=True,

colorscale='YlGnBu',code>

size=10,

colorbar=dict(

thickness=15,

title='Node Connections',code>

xanchor='left',code>

titleside='right'code>

),

line_width=2))

for node in G.nodes():

x, y = pos[node]

node_trace['x'] += [x]

node_trace['y'] += [y]

node_trace['text'] += [str(node)]

frames.append(go.Frame(data=[edge_trace, node_trace], name=str(step['time'])))

# 创建Plotly图表

fig = go.Figure(

data=[edge_trace, node_trace],

layout=go.Layout(

title='<br>Dynamic Network graph',code>

titlefont_size=16,

showlegend=False,

hovermode='closest',code>

margin=dict(b=20, l=5, r=5, t=40),

annotations=[dict(

text="Python code: <a href='https://plotly.com'> https://plotly.com </a>",code>

showarrow=False,

xref="paper", yref="paper",code>

x=0.005, y=-0.002)],

xaxis=dict(showgrid=False, zeroline=False),

yaxis=dict(showgrid=False, zeroline=False),

updatemenus=[dict(

type="buttons",code>

buttons=[dict(label="Play",code>

method="animate",code>

args=[None, { "frame": { "duration": 500, "redraw":

True}, "fromcurrent": True, "transition": { "duration": 300, "easing": "quadratic-in-out"}}]),

dict(label="Pause",code>

method="animate",code>

args=[[None], { "frame": { "duration": 0, "redraw": False}, "mode": "immediate",

"transition": { "duration": 0}}])])]

),

frames=frames

)

fig.show()

以上代码展示了如何使用Plotly创建一个动态网络图。我们定义了每个时间步长的网络结构，并使用Plotly的动画功能展示网络随时间的变化。通过点击播放按钮，用户可以看到网络节点和边的动态变化过程。

总结