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: plotly 6.7.0 | Python 3.13.13
Quality: 85/100 | Created: 2026-05-14
"""
import os
from collections import defaultdict
import numpy as np
import plotly.graph_objects as go
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"
STUDENT_COLOR = "#009E73" # Okabe-Ito position 1
COURSE_COLOR = "#C475FD" # Okabe-Ito position 2
EDGE_COLOR = "rgba(107,106,99,0.30)" if THEME == "light" else "rgba(168,167,159,0.22)"
# Data - student-course enrollment network
np.random.seed(42)
students = ["Alice", "Bob", "Carol", "David", "Emma", "Frank", "Grace", "Henry", "Iris", "Jack", "Karen", "Leo"]
courses = [
"Algorithms",
"Data Structures",
"Machine Learning",
"Statistics",
"Linear Algebra",
"Databases",
"Networks",
"Computer Vision",
]
# (student_idx, course_idx, weight) — weight encodes credit overlap strength
enrollments = [
(0, 0, 3),
(0, 1, 4),
(0, 2, 4),
(0, 3, 3),
(0, 4, 2), # Alice: 5 courses
(1, 0, 3),
(1, 5, 3),
(1, 7, 2),
(2, 2, 4),
(2, 3, 3),
(2, 6, 2),
(3, 1, 3),
(3, 4, 3),
(3, 6, 2),
(4, 0, 2),
(4, 2, 3),
(4, 7, 3),
(5, 5, 3),
(5, 6, 3),
(5, 7, 4),
(6, 1, 2),
(6, 3, 4),
(6, 4, 2),
(7, 2, 4),
(7, 5, 3),
(7, 6, 2),
(8, 0, 3),
(8, 1, 2),
(8, 3, 3),
(8, 7, 4), # Iris: 4 courses
(9, 4, 3),
(9, 6, 3), # Jack: 2 courses
(10, 5, 2),
(10, 7, 3), # Karen: 2 courses
(11, 3, 3),
(11, 6, 2),
(11, 7, 4),
]
# Degree per node
student_degree = [0] * len(students)
course_degree = [0] * len(courses)
for s_i, c_i, _ in enrollments:
student_degree[s_i] += 1
course_degree[c_i] += 1
# Node positions: students on left (x=0), courses on right (x=1)
student_y = np.linspace(0.05, 0.95, len(students))
course_y = np.linspace(0.10, 0.90, len(courses))
# Edge traces grouped by weight for variable line width
weight_groups = defaultdict(list)
for s_i, c_i, w in enrollments:
weight_groups[w].append((s_i, c_i))
traces = []
for w, pairs in sorted(weight_groups.items()):
xs, ys = [], []
for s_i, c_i in pairs:
xs += [0.0, 1.0, None]
ys += [float(student_y[s_i]), float(course_y[c_i]), None]
traces.append(
go.Scatter(
x=xs, y=ys, mode="lines", line={"width": w * 0.9, "color": EDGE_COLOR}, hoverinfo="none", showlegend=False
)
)
# Node size: linear scale on degree
s_min, s_max = min(student_degree), max(student_degree)
student_sizes = [22 + (d - s_min) / (s_max - s_min) * 28 for d in student_degree]
c_min, c_max = min(course_degree), max(course_degree)
course_sizes = [22 + (d - c_min) / (c_max - c_min) * 28 for d in course_degree]
traces.append(
go.Scatter(
x=[0.0] * len(students),
y=list(student_y),
mode="markers+text",
marker={"size": student_sizes, "color": STUDENT_COLOR, "line": {"color": PAGE_BG, "width": 2}},
text=students,
textposition="middle left",
textfont={"size": 16, "color": INK},
name="Students",
customdata=student_degree,
hovertemplate="<b>%{text}</b><br>Enrolled in %{customdata} courses<extra></extra>",
)
)
traces.append(
go.Scatter(
x=[1.0] * len(courses),
y=list(course_y),
mode="markers+text",
marker={"size": course_sizes, "color": COURSE_COLOR, "line": {"color": PAGE_BG, "width": 2}},
text=courses,
textposition="middle right",
textfont={"size": 16, "color": INK},
name="Courses",
customdata=course_degree,
hovertemplate="<b>%{text}</b><br>%{customdata} students enrolled<extra></extra>",
)
)
fig = go.Figure(data=traces)
fig.update_layout(
title={
"text": "Student-Course Enrollment · network-bipartite · plotly · anyplot.ai",
"font": {"size": 28, "color": INK},
"x": 0.5,
"xanchor": "center",
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
legend={
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 1,
"font": {"size": 16, "color": INK_SOFT},
"orientation": "h",
"x": 0.5,
"xanchor": "center",
"y": -0.04,
},
xaxis={"range": [-0.55, 1.55], "showgrid": False, "zeroline": False, "showticklabels": False, "showline": False},
yaxis={"range": [-0.05, 1.12], "showgrid": False, "zeroline": False, "showticklabels": False, "showline": False},
margin={"l": 20, "r": 20, "t": 80, "b": 60},
)
# Column headers
fig.add_annotation(
x=0.0, y=1.07, text="<b>Students</b>", font={"size": 22, "color": INK}, showarrow=False, xref="x", yref="y"
)
fig.add_annotation(
x=1.0, y=1.07, text="<b>Courses</b>", font={"size": 22, "color": INK}, showarrow=False, xref="x", yref="y"
)
# Subtle vertical separator
fig.add_shape(
type="line",
x0=0.5,
x1=0.5,
y0=0.0,
y1=1.0,
xref="x",
yref="paper",
line={"color": INK_SOFT, "width": 1, "dash": "dot"},
)
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Bipartite Network Graph on anyplot.ai.