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: bokeh 3.9.0 | Python 3.13.13
Quality: 84/100 | Created: 2026-05-14
"""
import sys
sys.path.pop(0) # prevent bokeh.py from shadowing the bokeh package
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
COLOR_A = "#009E73" # Students - Okabe-Ito position 1
COLOR_B = "#C475FD" # Courses - Okabe-Ito position 2
# Data: student-course enrollment network
np.random.seed(42)
students = [
"Alice",
"Bob",
"Carol",
"David",
"Emma",
"Frank",
"Grace",
"Henry",
"Iris",
"James",
"Kate",
"Liam",
"Maya",
"Noah",
"Olivia",
]
courses = [
"CS101",
"MATH201",
"PHYS101",
"BIO201",
"CHEM101",
"ENG101",
"HIST201",
"ART101",
"CS201",
"STAT101",
"ECON201",
"MUSIC101",
]
n_s = len(students)
n_c = len(courses)
# Generate enrollment edges (weighted toward popular courses)
course_weights = np.array([3, 3, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1], dtype=float)
course_weights /= course_weights.sum()
edges = set()
for i in range(n_s):
n_enroll = np.random.randint(2, 6)
chosen = np.random.choice(n_c, size=n_enroll, replace=False, p=course_weights)
for c in chosen:
edges.add((i, int(c)))
edges = sorted(edges)
# Node degrees
s_degree = np.zeros(n_s)
c_degree = np.zeros(n_c)
for s, c in edges:
s_degree[s] += 1
c_degree[c] += 1
# Layout: students left (x=0.22), courses right (x=0.78)
X_LEFT = 0.22
X_RIGHT = 0.78
s_y = np.linspace(0.92, 0.05, n_s)
c_y = np.linspace(0.92, 0.05, n_c)
# Node sizes proportional to degree
s_sizes = 28 + 52 * (s_degree / s_degree.max())
c_sizes = 28 + 52 * (c_degree / c_degree.max())
# Edge segment coordinates
seg_x0 = [X_LEFT] * len(edges)
seg_y0 = [s_y[s] for s, c in edges]
seg_x1 = [X_RIGHT] * len(edges)
seg_y1 = [c_y[c] for s, c in edges]
# Figure
p = figure(
width=4800,
height=2700,
x_range=(-0.05, 1.05),
y_range=(-0.02, 1.08),
toolbar_location=None,
tools="",
title="Student-Course Enrollment · network-bipartite · bokeh · anyplot.ai",
)
# Edges
p.segment(x0=seg_x0, y0=seg_y0, x1=seg_x1, y1=seg_y1, line_color=INK_MUTED, line_alpha=0.30, line_width=2)
# Student nodes (set A)
p.scatter(
x=[X_LEFT] * n_s, y=s_y.tolist(), size=s_sizes.tolist(), color=COLOR_A, line_color=PAGE_BG, line_width=4, alpha=0.92
)
# Course nodes (set B)
p.scatter(
x=[X_RIGHT] * n_c,
y=c_y.tolist(),
size=c_sizes.tolist(),
color=COLOR_B,
line_color=PAGE_BG,
line_width=4,
alpha=0.92,
)
# Student labels (right-aligned, left of nodes)
p.text(
x=[X_LEFT - 0.03] * n_s,
y=s_y.tolist(),
text=students,
text_align="right",
text_baseline="middle",
text_font_size="18pt",
text_color=INK_SOFT,
)
# Course labels (left-aligned, right of nodes)
p.text(
x=[X_RIGHT + 0.03] * n_c,
y=c_y.tolist(),
text=courses,
text_align="left",
text_baseline="middle",
text_font_size="18pt",
text_color=INK_SOFT,
)
# Column headers (colored to match node sets)
p.text(
x=[X_LEFT, X_RIGHT],
y=[1.03, 1.03],
text=["Students", "Courses"],
text_align="center",
text_baseline="middle",
text_font_size="24pt",
text_font_style="bold",
text_color=[COLOR_A, COLOR_B],
)
# Theme-adaptive chrome
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = None
p.xaxis.visible = False
p.yaxis.visible = False
p.xgrid.visible = False
p.ygrid.visible = False
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.title.text_font_style = "normal"
# Save HTML + PNG
output_file(f"plot-{THEME}.html")
save(p)
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Bipartite Network Graph on anyplot.ai.