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: altair 6.1.0 | Python 3.13.13
Quality: 89/100 | Created: 2026-05-14
"""
import os
import sys
# Remove script directory from sys.path to avoid importing local altair.py
script_dir = os.path.dirname(os.path.abspath(__file__))
if script_dir in sys.path:
sys.path.remove(script_dir)
import altair as alt
import numpy as np
import pandas as pd
np.random.seed(42)
# Theme tokens
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_A = "#009E73" # Authors — Okabe-Ito position 1
COLOR_B = "#C475FD" # Papers — Okabe-Ito position 2
# Data — researcher-paper affiliation network (bibliometrics)
author_names = [
"Chen, L.",
"Smith, R.",
"Patel, A.",
"Kim, J.",
"Müller, K.",
"Davis, M.",
"López, C.",
"Nguyen, T.",
"Brown, E.",
"Ivanova, S.",
"Ahmed, F.",
"Wilson, G.",
]
paper_ids = [f"P{i + 1:02d}" for i in range(15)]
# Edges: each author co-authors 2–4 papers
edge_list = []
for author in author_names:
n = np.random.randint(2, 5)
selected = np.random.choice(paper_ids, size=n, replace=False)
for p in selected:
edge_list.append({"source": author, "target": p})
edges_df = pd.DataFrame(edge_list).drop_duplicates(subset=["source", "target"])
# Node degrees
src_deg = edges_df.groupby("source").size().reset_index(name="degree")
tgt_deg = edges_df.groupby("target").size().reset_index(name="degree")
# Node positions — two columns, evenly spaced vertically
nodes_a = pd.DataFrame(
{"node": author_names, "x": 0.22, "y": np.linspace(0.08, 0.88, len(author_names)), "set": "Author"}
).merge(src_deg.rename(columns={"source": "node"}), on="node", how="left")
nodes_a["degree"] = nodes_a["degree"].fillna(0).astype(int)
nodes_b = pd.DataFrame(
{"node": paper_ids, "x": 0.78, "y": np.linspace(0.08, 0.88, len(paper_ids)), "set": "Paper"}
).merge(tgt_deg.rename(columns={"target": "node"}), on="node", how="left")
nodes_b["degree"] = nodes_b["degree"].fillna(0).astype(int)
nodes_df = pd.concat([nodes_a, nodes_b], ignore_index=True)
# Edge coordinates for mark_rule (x,y → x2,y2)
node_pos = nodes_df.set_index("node")[["x", "y"]].to_dict("index")
edge_records = []
for _, row in edges_df.iterrows():
s, t = node_pos[row["source"]], node_pos[row["target"]]
edge_records.append({"x": s["x"], "y": s["y"], "x2": t["x"], "y2": t["y"]})
edge_data = pd.DataFrame(edge_records)
# Shared scale objects
xscale = alt.Scale(domain=[0.0, 1.0])
yscale = alt.Scale(domain=[0.0, 1.05])
TITLE = "network-bipartite · altair · anyplot.ai"
# Edge lines
edges_chart = (
alt.Chart(edge_data)
.mark_rule(color=INK_SOFT, opacity=0.28, strokeWidth=1.2)
.encode(x=alt.X("x:Q", scale=xscale, axis=None), y=alt.Y("y:Q", scale=yscale, axis=None), x2="x2:Q", y2="y2:Q")
)
# Nodes — size encodes degree, color encodes set membership
nodes_chart = (
alt.Chart(nodes_df)
.mark_circle(stroke=PAGE_BG, strokeWidth=2)
.encode(
x=alt.X("x:Q", scale=xscale, axis=None),
y=alt.Y("y:Q", scale=yscale, axis=None),
color=alt.Color(
"set:N",
scale=alt.Scale(domain=["Author", "Paper"], range=[COLOR_A, COLOR_B]),
legend=alt.Legend(title="Node Set", titleFontSize=20, labelFontSize=18, orient="bottom-right"),
),
size=alt.Size("degree:Q", scale=alt.Scale(range=[200, 1200]), legend=None),
tooltip=["node:N", "set:N", "degree:N"],
)
)
# Node labels — authors right-aligned, papers left-aligned
labels_a = (
alt.Chart(nodes_a[["node", "x", "y"]])
.mark_text(align="right", dx=-30, fontSize=15)
.encode(
x=alt.X("x:Q", scale=xscale, axis=None),
y=alt.Y("y:Q", scale=yscale, axis=None),
text="node:N",
color=alt.value(INK_SOFT),
)
)
labels_b = (
alt.Chart(nodes_b[["node", "x", "y"]])
.mark_text(align="left", dx=30, fontSize=15)
.encode(
x=alt.X("x:Q", scale=xscale, axis=None),
y=alt.Y("y:Q", scale=yscale, axis=None),
text="node:N",
color=alt.value(INK_SOFT),
)
)
# Column header labels
header_a = (
alt.Chart(pd.DataFrame({"x": [0.22], "y": [0.97], "text": ["Authors"]}))
.mark_text(fontSize=22, fontWeight="bold", color=COLOR_A)
.encode(x=alt.X("x:Q", scale=xscale, axis=None), y=alt.Y("y:Q", scale=yscale, axis=None), text="text:N")
)
header_b = (
alt.Chart(pd.DataFrame({"x": [0.78], "y": [0.97], "text": ["Papers"]}))
.mark_text(fontSize=22, fontWeight="bold", color=COLOR_B)
.encode(x=alt.X("x:Q", scale=xscale, axis=None), y=alt.Y("y:Q", scale=yscale, axis=None), text="text:N")
)
# Compose all layers
chart = (
alt.layer(edges_chart, nodes_chart, labels_a, labels_b, header_a, header_b)
.properties(width=1600, height=900, title=alt.Title(TITLE), background=PAGE_BG)
.configure_view(fill=PAGE_BG, strokeOpacity=0)
.configure_axis(
domainColor=INK_SOFT,
tickColor=INK_SOFT,
gridColor=INK,
gridOpacity=0.10,
labelColor=INK_SOFT,
titleColor=INK,
labelFontSize=18,
titleFontSize=22,
)
.configure_title(color=INK, fontSize=28, anchor="start", offset=12)
.configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
)
# Save
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Bipartite Network Graph on anyplot.ai.