A bipartite network graph visualizes relationships between two distinct sets of entities, where edges only connect nodes from different sets — never within the same set. The two node groups are arranged in separate columns or rows, making the two-mode structure immediately apparent. This layout is fundamental for understanding cross-category relationships, revealing which entities from one set are linked to which entities in the other, and exposing patterns like hubs, clusters, and isolated nodes.
""" anyplot.ai
network-bipartite: Bipartite Network Graph
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 84/100 | Created: 2026-05-14
"""
import os
import sys
# Workaround for module/filename conflict: remove current dir from path persistently
sys.path = [p for p in sys.path if not p.startswith(os.path.dirname(os.path.abspath(__file__)))]
import numpy as np
import pandas as pd
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_point,
geom_segment,
geom_text,
ggplot,
labs,
scale_color_manual,
scale_size_continuous,
scale_x_continuous,
scale_y_continuous,
theme,
)
np.random.seed(42)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
COLOR_GENE = "#009E73" # Okabe-Ito position 1
COLOR_DISEASE = "#4467A3" # Okabe-Ito position 3
# Data: gene–disease association network in bioinformatics
genes = ["BRCA1", "TP53", "PTEN", "EGFR", "KRAS", "PIK3CA", "APC", "RB1", "VHL", "CDKN2A", "MLH1", "ERBB2"]
diseases = [
"Breast Cancer",
"Lung Cancer",
"Colorectal Cancer",
"Prostate Cancer",
"Glioblastoma",
"Melanoma",
"Ovarian Cancer",
"Leukemia",
]
# (gene_index, disease_index) edges
connections = [
(0, 0),
(0, 6),
(1, 0),
(1, 1),
(1, 2),
(1, 4),
(2, 0),
(2, 3),
(3, 1),
(3, 4),
(4, 1),
(4, 2),
(5, 0),
(5, 2),
(5, 3),
(6, 2),
(7, 0),
(7, 7),
(8, 5),
(9, 1),
(9, 5),
(10, 2),
(10, 7),
(11, 0),
(11, 1),
]
gene_y = np.linspace(0.05, 0.95, len(genes))
disease_y = np.linspace(0.10, 0.90, len(diseases))
gene_df = pd.DataFrame(
{
"label": genes,
"x": 0.0,
"y": gene_y,
"node_set": "Gene",
"degree": [sum(1 for g, _ in connections if g == i) for i in range(len(genes))],
}
)
disease_df = pd.DataFrame(
{
"label": diseases,
"x": 1.0,
"y": disease_y,
"node_set": "Disease",
"degree": [sum(1 for _, d in connections if d == i) for i in range(len(diseases))],
}
)
nodes = pd.concat([gene_df, disease_df], ignore_index=True)
edges = pd.DataFrame(
{
"x": [gene_df.iloc[g]["x"] for g, _ in connections],
"y": [gene_df.iloc[g]["y"] for g, _ in connections],
"xend": [disease_df.iloc[d]["x"] for _, d in connections],
"yend": [disease_df.iloc[d]["y"] for _, d in connections],
}
)
# Plot
plot = (
ggplot()
+ geom_segment(aes(x="x", y="y", xend="xend", yend="yend"), data=edges, color=INK_SOFT, alpha=0.30, size=0.6)
+ geom_point(aes(x="x", y="y", color="node_set", size="degree"), data=nodes)
+ geom_text(aes(x="x", y="y", label="label", color="node_set"), data=gene_df, ha="right", nudge_x=-0.04, size=10)
+ geom_text(aes(x="x", y="y", label="label", color="node_set"), data=disease_df, ha="left", nudge_x=0.04, size=10)
+ scale_color_manual(values={"Gene": COLOR_GENE, "Disease": COLOR_DISEASE}, name="Node Type")
+ scale_size_continuous(range=(4, 14), name="Degree")
+ scale_x_continuous(limits=(-0.55, 1.55), breaks=[0, 1], labels=["Genes", "Diseases"])
+ scale_y_continuous(limits=(-0.05, 1.05))
+ labs(title="network-bipartite · plotnine · anyplot.ai", x="", y="")
+ theme(
figure_size=(16, 9),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_blank(),
panel_grid_minor=element_blank(),
panel_border=element_blank(),
axis_title=element_blank(),
axis_text_x=element_text(color=INK, size=22, face="bold"),
axis_text_y=element_blank(),
axis_ticks_major_y=element_blank(),
axis_ticks_minor_y=element_blank(),
axis_line_x=element_line(color=INK_SOFT, size=0.5),
plot_title=element_text(color=INK, size=24),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=16),
legend_title=element_text(color=INK, size=16),
)
)
plot.save(f"plot-{THEME}.png", dpi=300, width=16, height=9)
Part of Bipartite Network Graph on anyplot.ai.