Network Visualization
Visualize biological networks with static (matplotlib), interactive (PyVis), and publication-quality (Cytoscape) approaches.
NetworkX + Matplotlib
Basic Network Plot
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
G = nx.karate_club_graph()
fig, ax = plt.subplots(figsize=(10, 8))
pos = nx.spring_layout(G, seed=42)
nx.draw_networkx(G, pos, node_size=300, node_color='#4DBBD5', edge_color='gray',
font_size=8, width=0.8, ax=ax)
ax.set_title('Network')
ax.axis('off')
plt.tight_layout()
plt.savefig('network.png', dpi=300, bbox_inches='tight')
Layout Algorithms
| Layout |
Function |
Best For |
| Spring |
nx.spring_layout(G, k=1.5, seed=42) |
General-purpose, force-directed |
| Kamada-Kawai |
nx.kamada_kawai_layout(G) |
Small-medium networks, clean separation |
| Circular |
nx.circular_layout(G) |
Showing all connections, small networks |
| Shell |
nx.shell_layout(G, nlist=[core, periphery]) |
Hub-spoke topology |
| Spectral |
nx.spectral_layout(G) |
Revealing clusters |
The spring layout k parameter controls node spacing: increase for sparse layouts, decrease for compact. Always set seed for reproducibility.
Degree-Based Node Sizing
def draw_network(G, pos=None, title='', ax=None, node_cmap='YlOrRd'):
'''Draw network with degree-proportional node sizes and colored by degree'''
if pos is None:
pos = nx.spring_layout(G, seed=42, k=1.5)
if ax is None:
fig, ax = plt.subplots(figsize=(10, 8))
degrees = dict(G.degree())
node_sizes = [100 + degrees[n] * 150 for n in G.nodes()]
node_colors = [degrees[n] for n in G.nodes()]
nx.draw_networkx_edges(G, pos, alpha=0.3, edge_color='gray', width=0.8, ax=ax)
nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=node_colors,
cmap=plt.cm.get_cmap(node_cmap), edgecolors='black',
linewidths=0.5, ax=ax)
nx.draw_networkx_labels(G, pos, font_size=7, ax=ax)
plt.colorbar(nodes, ax=ax, label='Degree', shrink=0.8)
ax.set_title(title)
ax.axis('off')
return ax
Edge Weight Visualization
def draw_weighted_network(G, pos=None, weight_key='weight'):
'''Draw network with edge width proportional to weight'''
if pos is None:
pos = nx.spring_layout(G, seed=42, k=1.5)
fig, ax = plt.subplots(figsize=(10, 8))
weights = [G[u][v].get(weight_key, 1.0) for u, v in G.edges()]
# Normalize weights to [0.5, 4] for visible edge widths
min_w, max_w = min(weights), max(weights)
if max_w > min_w:
scaled = [0.5 + 3.5 * (w - min_w) / (max_w - min_w) for w in weights]
else:
scaled = [1.0] * len(weights)
nx.draw_networkx_edges(G, pos, width=scaled, alpha=0.5, edge_color='gray', ax=ax)
nx.draw_networkx_nodes(G, pos, node_size=400, node_color='#4DBBD5', edgecolors='black', linewidths=0.5, ax=ax)
nx.draw_networkx_labels(G, pos, font_size=8, ax=ax)
ax.axis('off')
plt.tight_layout()
return fig, ax
Community Detection Coloring
from networkx.algorithms.community import greedy_modularity_communities
def draw_communities(G, pos=None):
'''Color nodes by community membership'''
if pos is None:
pos = nx.spring_layout(G, seed=42, k=1.5)
communities = list(greedy_modularity_communities(G))
node_to_community = {}
for i, comm in enumerate(communities):
for node in comm:
node_to_community[node] = i
node_colors = [node_to_community[n] for n in G.nodes()]
palette = plt.cm.get_cmap('Set2', len(communities))
fig, ax = plt.subplots(figsize=(10, 8))
nx.draw_networkx_edges(G, pos, alpha=0.2, edge_color='gray', ax=ax)
nx.draw_networkx_nodes(G, pos, node_size=400, node_color=node_colors, cmap=palette,
edgecolors='black', linewidths=0.5, ax=ax)
nx.draw_networkx_labels(G, pos, font_size=7, ax=ax)
for i, comm in enumerate(communities):
ax.scatter([], [], c=[palette(i)], label=f'Community {i+1} ({len(comm)} nodes)', s=80)
ax.legend(loc='upper left', fontsize=8, framealpha=0.9)
ax.set_title(f'Network Communities (modularity, {len(communities)} groups)')
ax.axis('off')
plt.tight_layout()
return fig, ax
Highlight Subnetwork
def highlight_genes(G, highlight_nodes, pos=None, highlight_color='#E64B35', default_color='#cccccc'):
'''Highlight specific nodes (e.g., DE genes) in a network'''
if pos is None:
pos = nx.spring_layout(G, seed=42, k=1.5)
colors = [highlight_color if n in highlight_nodes else default_color for n in G.nodes()]
sizes = [600 if n in highlight_nodes else 200 for n in G.nodes()]
font_sizes = {n: 9 if n in highlight_nodes else 0 for n in G.nodes()}
fig, ax = plt.subplots(figsize=(10, 8))
nx.draw_networkx_edges(G, pos, alpha=0.15, edge_color='gray', ax=ax)
nx.draw_networkx_nodes(G, pos, node_size=sizes, node_color=colors, edgecolors='black', linewidths=0.5, ax=ax)
labels = {n: n for n in highlight_nodes if n in G.nodes()}
nx.draw_networkx_labels(G, pos, labels=labels, font_size=9, font_weight='bold', ax=ax)
ax.axis('off')
plt.tight_layout()
return fig, ax
PyVis Interactive Networks
Basic Interactive Plot
from pyvis.network import Network
def interactive_network(G, output='network.html', height='700px', width='100%'):
'''Create interactive HTML network with PyVis'''
net = Network(height=height, width=width, bgcolor='white', font_color='black')
net.from_nx(G)
net.toggle_physics(True)
net.show_buttons(filter_=['physics'])
net.save_graph(output)
return output
Styled Interactive Network
def styled_interactive_network(G, output='styled_network.html', community_colors=None):
'''Interactive network with degree-based sizing and community colors'''
net = Network(height='700px', width='100%', bgcolor='white', font_color='black')
degrees = dict(G.degree())
palette = ['#E64B35', '#4DBBD5', '#00A087', '#3C5488', '#F39B7F', '#8491B4', '#91D1C2', '#DC0000']
for node in G.nodes():
size = 10 + degrees[node] * 5
if community_colors and node in community_colors:
color = palette[community_colors[node] % len(palette)]
else:
color = '#4DBBD5'
net.add_node(node, size=size, color=color, title=f'{node}\nDegree: {degrees[node]}')
for u, v, data in G.edges(data=True):
weight = data.get('weight', 1.0)
net.add_edge(u, v, width=weight * 2, title=f'Weight: {weight:.2f}')
net.toggle_physics(True)
net.set_options('{"physics": {"forceAtlas2Based": {"gravitationalConstant": -50}}}')
net.save_graph(output)
return output
PyVis Configuration Options
| Option |
Values |
Effect |
toggle_physics(True) |
True/False |
Enable force-directed layout |
show_buttons() |
filter list |
UI controls for layout tuning |
barnes_hut() |
- |
Fast layout for large networks |
force_atlas_2based() |
- |
Good cluster separation |
repulsion() |
- |
Uniform spacing |
Cytoscape Automation
py4cytoscape Basics
import py4cytoscape as p4c
def send_to_cytoscape(G, title='Network', layout='force-directed'):
'''Send NetworkX graph to Cytoscape (must be running)'''
p4c.create_network_from_networkx(G, title=title)
p4c.layout_network(layout)
return p4c.get_network_suid()
def style_by_degree(network_suid=None):
'''Apply degree-proportional node sizing in Cytoscape'''
style_name = 'DegreeStyle'
p4c.create_visual_style(style_name)
p4c.set_node_size_mapping('degree', [1, 5, 20], [30, 60, 120],
mapping_type='c', style_name=style_name)
p4c.set_node_color_mapping('degree', [1, 10, 20], ['#FFFFCC', '#FD8D3C', '#BD0026'],
mapping_type='c', style_name=style_name)
p4c.set_visual_style(style_name)
Export from Cytoscape
def export_cytoscape_figure(filename='network.pdf', resolution=300):
'''Export current Cytoscape view as publication figure'''
if filename.endswith('.pdf'):
p4c.export_image(filename, type='PDF')
else:
p4c.export_image(filename, type='PNG', resolution=resolution)
Cytoscape Layout Options
| Layout |
Use Case |
force-directed |
General-purpose, good default |
circular |
Small networks, all connections visible |
hierarchical |
Signaling cascades, directed networks |
grid |
Uniform placement for comparison |
degree-circle |
Hub nodes in center |
Bioinformatics Styling Patterns
PPI Network Styling
def style_ppi_network(G, pos=None, score_key='score'):
'''Style for protein-protein interaction networks'''
if pos is None:
pos = nx.kamada_kawai_layout(G)
fig, ax = plt.subplots(figsize=(12, 10))
degrees = dict(G.degree())
node_sizes = [200 + degrees[n] * 100 for n in G.nodes()]
edges = G.edges(data=True)
edge_colors = ['#E64B35' if d.get(score_key, 0) >= 900
else '#F39B7F' if d.get(score_key, 0) >= 700
else '#cccccc' for _, _, d in edges]
nx.draw_networkx_edges(G, pos, edge_color=edge_colors, width=1.2, alpha=0.6, ax=ax)
nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='#4DBBD5',
edgecolors='black', linewidths=0.5, ax=ax)
labels = {n: n for n in G.nodes() if degrees[n] >= 3}
nx.draw_networkx_labels(G, pos, labels=labels, font_size=8, font_weight='bold', ax=ax)
ax.axis('off')
plt.tight_layout()
return fig, ax
GRN Directed Network
def draw_grn(G, pos=None):
'''Draw gene regulatory network with directed edges and TF highlighting'''
if pos is None:
pos = nx.spring_layout(G, seed=42, k=2.0)
fig, ax = plt.subplots(figsize=(12, 10))
activating = [(u, v) for u, v, d in G.edges(data=True) if d.get('regulation') == 'activation']
repressing = [(u, v) for u, v, d in G.edges(data=True) if d.get('regulation') == 'repression']
nx.draw_networkx_edges(G, pos, edgelist=activating, edge_color='#00A087',
arrows=True, arrowsize=15, width=1.5, alpha=0.7, ax=ax)
nx.draw_networkx_edges(G, pos, edgelist=repressing, edge_color='#E64B35',
arrows=True, arrowsize=15, width=1.5, alpha=0.7,
style='dashed', ax=ax)
tfs = [n for n in G.nodes() if G.out_degree(n) > 0]
targets = [n for n in G.nodes() if n not in tfs]
nx.draw_networkx_nodes(G, pos, nodelist=tfs, node_size=600, node_color='#3C5488',
node_shape='D', edgecolors='black', linewidths=0.5, ax=ax)
nx.draw_networkx_nodes(G, pos, nodelist=targets, node_size=300, node_color='#F39B7F',
edgecolors='black', linewidths=0.5, ax=ax)
nx.draw_networkx_labels(G, pos, font_size=7, ax=ax)
ax.scatter([], [], c='#00A087', marker='>', s=60, label='Activation')
ax.scatter([], [], c='#E64B35', marker='>', s=60, label='Repression')
ax.scatter([], [], c='#3C5488', marker='D', s=60, label='TF')
ax.scatter([], [], c='#F39B7F', marker='o', s=60, label='Target')
ax.legend(loc='upper left', fontsize=9, framealpha=0.9)
ax.axis('off')
plt.tight_layout()
return fig, ax
Multi-Panel Network Comparison
def compare_networks(graphs, titles, ncols=2):
'''Side-by-side network comparison'''
nrows = (len(graphs) + ncols - 1) // ncols
fig, axes = plt.subplots(nrows, ncols, figsize=(7 * ncols, 6 * nrows))
axes = axes.flatten() if hasattr(axes, 'flatten') else [axes]
for ax, G, title in zip(axes, graphs, titles):
pos = nx.spring_layout(G, seed=42, k=1.5)
degrees = dict(G.degree())
sizes = [100 + degrees[n] * 100 for n in G.nodes()]
nx.draw_networkx_edges(G, pos, alpha=0.2, edge_color='gray', ax=ax)
nx.draw_networkx_nodes(G, pos, node_size=sizes, node_color='#4DBBD5',
edgecolors='black', linewidths=0.5, ax=ax)
nx.draw_networkx_labels(G, pos, font_size=6, ax=ax)
ax.set_title(f'{title}\n({G.number_of_nodes()} nodes, {G.number_of_edges()} edges)', fontsize=10)
ax.axis('off')
for ax in axes[len(graphs):]:
ax.set_visible(False)
plt.tight_layout()
return fig
Export Formats
| Format |
Method |
Use Case |
| PNG |
plt.savefig('net.png', dpi=300) |
Presentations, web |
| PDF |
plt.savefig('net.pdf') |
Publication figures |
| SVG |
plt.savefig('net.svg') |
Editable vector graphics |
| HTML |
net.save_graph('net.html') |
Interactive sharing (PyVis) |
| GraphML |
nx.write_graphml(G, 'net.graphml') |
Import to Cytoscape |
| GML |
nx.write_gml(G, 'net.gml') |
Import to Gephi |
Related Skills
- data-visualization/multipanel-figures - Combine network panels with other plots
- gene-regulatory-networks/coexpression-networks - Build co-expression networks to visualize
- database-access/interaction-databases - Fetch PPI data for network construction
tools-only