Non-ribbon Chord Diagram
Chart overview
Non-ribbon chord diagrams arrange nodes in a circle and connect them with simple lines instead of width-varying ribbons.
Key points
- This variant works well when connection strength is less important than the existence of connections, common in biological and social network analysis.
- Dropping the ribbon width is a deliberate trade: you gain a clean view of which nodes connect (topology) and lose the ability to compare edge magnitudes, so reach for it when the data is binary or near-binary adjacency - gene-gene interactions, co-authorship, shared-membership - and use a full ribbon chord diagram when flow volume is the point.
- The circular layout's strength is that every node is equally visible on the rim; its weakness is that edges cross through the middle and become a hairball past roughly 30 nodes or a few hundred edges.
Practical guidance
Two standard mitigations: order nodes around the circle to minimize crossings (group by cluster or community, not alphabetically) so related connections stay as short arcs, and bundle edges toward the center (hierarchical edge bundling) to turn a mess of straight chords into legible curved cables. In Python, networkx computes a circular_layout and community structure for coloring; drawing bundled arcs usually means Bezier curves toward the centroid. If node degree or a per-node metric matters, size or color the rim segments. When the graph is large or the exact wiring matters more than the circular aesthetic, a force-directed node-link layout or an adjacency matrix will read more clearly.
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Python Tutorial
How to create a non-ribbon chord diagram in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a circular network diagram (non-ribbon chord diagram) showing 'Gene Interaction Network' for 15 genes involved in a biological pathway. Generate realistic gene interaction data: each gene (TP53, BRCA1, EGFR, MYC, KRAS, etc.) with 2-5 connections representing protein-protein interactions. Position genes in a circle using their pathway order. Draw straight lines between interacting genes. Line color by interaction type: activation (green), inhibition (red), binding (blue). Line thickness by confidence score (0.5-1.0). Node size by number of connections (degree). Label each gene node. Highlight hub genes (>4 connections) with a larger marker. Include a legend for interaction types."
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Python Code Example
# === IMPORTS ===
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from matplotlib.patches import Arc, FancyArrowPatch
import matplotlib.patheffects as pe
# === USER-EDITABLE PARAMETERS ===
title = "Gene Regulatory Network — Circular Layout"
figsize = (14, 14)
# === EXAMPLE DATASET ===
# Gene names and pathway involvement
genes = [
('TP53', 'Tumor Suppressor'),
('BRCA1', 'DNA Repair'),
('EGFR', 'Growth Factor'),
('MYC', 'Transcription Factor'),
('KRAS', 'Signal Transduction'),
('AKT1', 'Cell Survival'),
('PIK3CA', 'Signal Transduction'),
('PTEN', 'Tumor Suppressor'),
('RB1', 'Cell Cycle'),
('CDK4', 'Cell Cycle'),
('CDKN2A', 'Tumor Suppressor'),
('MDM2', 'P53 Regulation'),
]
# Interactions: (gene1, gene2, type, confidence)
interactions = [
('TP53', 'MDM2', 'inhibition', 0.97),
('MDM2', 'TP53', 'inhibition', 0.95),
('TP53', 'CDKN2A', 'activation', 0.88),
('BRCA1', 'TP53', 'binding', 0.92),
('BRCA1', 'RB1', 'binding', 0.78),
('EGFR', 'PIK3CA', 'activation', 0.94),
('EGFR', 'KRAS', 'activation', 0.91),
('MYC', 'CDK4', 'activation', 0.87),
('KRAS', 'PIK3CA', 'activation', 0.89),
('AKT1', 'PTEN', 'inhibition', 0.93),
('PIK3CA', 'AKT1', 'activation', 0.96),
('PTEN', 'PIK3CA', 'inhibition', 0.91),
('RB1', 'CDK4', 'inhibition', 0.90),
('CDK4', 'RB1', 'inhibition', 0.88),
('CDKN2A', 'CDK4', 'inhibition', 0.92),
]
# Create graph
G = nx.DiGraph()
for gene, pathway in genes:
G.add_node(gene, pathway=pathway)
for gene1, gene2, int_type, conf in interactions:
G.add_edge(gene1, gene2, type=int_type, confidence=conf)
# Print summary
print("=== Gene Regulatory Network ===")
print(f"\nGenes: {len(genes)}")
print(f"Interactions: {len(interactions)}")
print(f"\nHub genes (degree > 3):")
degrees = dict(G.degree())
for gene, degree in sorted(degrees.items(), key=lambda x: x[1], reverse=True):
if degree > 3:
print(f" {gene}: {degree} connections")
# === CREATE CIRCULAR NETWORK DIAGRAM ===
fig, ax = plt.subplots(figsize=figsize, facecolor='#0d1117')
ax.set_facecolor('#0d1117')
# Circular layout
pos = nx.circular_layout(G)
# Pathway colors
pathway_colors = {
'Tumor Suppressor': '#FF6B6B',
'DNA Repair': '#4ECDC4',
'Growth Factor': '#45B7D1',
'Transcription Factor': '#96CEB4',
'Signal Transduction': '#FFEAA7',
'Cell Survival': '#DDA0DD',
'Cell Cycle': '#98D8C8',
'P53 Regulation': '#F7DC6F'
}
# Edge colors by interaction type
edge_color_map = {
'activation': '#00E676',
'inhibition': '#FF5252',
'binding': '#448AFF'
}
# Draw curved edges
for gene1, gene2, data in G.edges(data=True):
x1, y1 = pos[gene1]
x2, y2 = pos[gene2]
color = edge_color_map[data['type']]
width = data['confidence'] * 3
# Create curved arrow
style = "arc3,rad=0.2"
arrow = FancyArrowPatch(
(x1, y1), (x2, y2),
connectionstyle=style,
arrowstyle='-|>',
mutation_scale=15,
color=color,
linewidth=width,
alpha=0.7
)
ax.add_patch(arrow)
# Draw nodes with glow
for gene, pathway in genes:
x, y = pos[gene]
color = pathway_colors[pathway]
size = degrees[gene] * 80 + 200
# Glow effect
for i in range(4, 0, -1):
circle = plt.Circle((x, y), 0.08 * i, color=color, alpha=0.05)
ax.add_patch(circle)
# Main node
ax.scatter(x, y, s=size, c=color, edgecolors='white',
linewidths=2, zorder=10, alpha=0.95)
# Draw labels
label_offset = 0.15
for gene, _ in genes:
x, y = pos[gene]
# Position label outside the circle
angle = np.arctan2(y, x)
lx = x + label_offset * np.cos(angle)
ly = y + label_offset * np.sin(angle)
rotation = np.degrees(angle)
if rotation > 90:
rotation -= 180
elif rotation < -90:
rotation += 180
ax.text(lx, ly, gene, fontsize=11, fontweight='bold',
ha='center', va='center', color='white',
rotation=rotation,
path_effects=[pe.withStroke(linewidth=3, foreground='#0d1117')])
# Legend for edge types
from matplotlib.lines import Line2D
edge_legend = [
Line2D([0], [0], color='#00E676', linewidth=3, label='Activation →'),
Line2D([0], [0], color='#FF5252', linewidth=3, label='Inhibition ⊣'),
Line2D([0], [0], color='#448AFF', linewidth=3, label='Binding â—‹'),
]
legend1 = ax.legend(handles=edge_legend, loc='upper left',
facecolor='#161b22', edgecolor='#30363d',
labelcolor='white', fontsize=10, title='Interaction Type',
title_fontsize=11)
legend1.get_title().set_color('white')
# Legend for pathways
from matplotlib.patches import Patch
pathway_legend = [
Patch(facecolor=color, label=pathway, edgecolor='white')
for pathway, color in list(pathway_colors.items())[:6]
]
legend2 = ax.legend(handles=pathway_legend, loc='upper right',
facecolor='#161b22', edgecolor='#30363d',
labelcolor='white', fontsize=9, title='Pathway',
title_fontsize=10)
legend2.get_title().set_color('white')
ax.add_artist(legend1)
# Title
ax.set_title(title, fontsize=22, fontweight='bold', color='white', pad=20,
path_effects=[pe.withStroke(linewidth=3, foreground='#238636')])
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal')
ax.axis('off')
plt.tight_layout()
plt.savefig('chart.png', dpi=150, bbox_inches='tight', facecolor='#0d1117')
print("Saved: chart.png")
plt.show()
# END-OF-CODE
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Console Output
=== Gene Regulatory Network === Genes: 12 Interactions: 15 Hub genes (degree > 3): TP53: 4 connections PIK3CA: 4 connections CDK4: 4 connections Saved: chart.png
Common Use Cases
- 1Gene interaction networks
- 2Protein-protein interactions
- 3Simple relationship mapping
- 4Communication patterns
Pro Tips
Order nodes to minimize crossings
Use edge bundling for many connections
Color edges by relationship type
Frequently asked questions
When should you use a non-ribbon chord diagram?
Non-ribbon chord diagrams arrange nodes in a circle and connect them with simple lines instead of width-varying ribbons. This variant works well when connection strength is less important than the existence of connections, common in biological and social network analysis. Common applications include gene interaction networks, protein-protein interactions, and simple relationship mapping.
Which Python libraries can create a non-ribbon chord diagram?
A non-ribbon chord diagram can be built in Python with networkx — networkx. In Plotivy you describe the figure and it writes the networkx code for you.
Can I make a non-ribbon chord diagram without writing Python code?
Yes. Describe the non-ribbon chord diagram you need in plain language and upload your dataset — Plotivy's AI writes the Python code and renders a publication-ready figure. You still get the full, editable networkx source, so nothing is locked in a black box.
What are best practices for a clear non-ribbon chord diagram?
Order nodes to minimize crossings. Use edge bundling for many connections.
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
networkx
Useful in specialized workflows that complement core Python plotting libraries for non-ribbon-chord-diagram analysis tasks.
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