A hive plot arranges network nodes on radial axes based on node properties (such as degree, category, or other attributes), enabling reproducible and directly comparable network visualizations. Unlike force-directed layouts which can produce different arrangements for identical networks, hive plots always render the same network identically, solving the "hairball" problem of traditional network graphs and making structural comparisons reliable.
""" anyplot.ai
hive-basic: Basic Hive Plot
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 95/100 | Updated: 2026-05-07
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_fixed,
element_rect,
element_text,
geom_point,
geom_segment,
geom_text,
ggplot,
labs,
scale_color_manual,
theme,
theme_void,
xlim,
ylim,
)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
BRAND = "#009E73"
COLOR_2 = "#C475FD"
COLOR_3 = "#4467A3"
np.random.seed(42)
nodes = pd.DataFrame(
{
"id": [
"auth",
"db",
"core",
"session",
"kernel",
"runtime",
"engine",
"cache",
"logger",
"config",
"validator",
"crypto",
"parser",
"queue",
"api",
"web",
"cli",
"router",
"http",
"grpc",
"websocket",
],
"category": [
"core",
"core",
"core",
"core",
"core",
"core",
"core",
"utility",
"utility",
"utility",
"utility",
"utility",
"utility",
"utility",
"interface",
"interface",
"interface",
"interface",
"interface",
"interface",
"interface",
],
"degree": [8, 7, 9, 6, 5, 4, 6, 5, 6, 4, 5, 4, 3, 4, 6, 5, 4, 5, 5, 3, 4],
}
)
edges = pd.DataFrame(
{
"source": [
"api",
"api",
"api",
"web",
"web",
"web",
"cli",
"cli",
"auth",
"auth",
"auth",
"db",
"db",
"cache",
"logger",
"config",
"validator",
"core",
"core",
"core",
"router",
"router",
"session",
"session",
"http",
"crypto",
"grpc",
"websocket",
"kernel",
"runtime",
],
"target": [
"auth",
"db",
"logger",
"auth",
"session",
"router",
"config",
"logger",
"db",
"crypto",
"session",
"cache",
"logger",
"logger",
"config",
"validator",
"logger",
"db",
"cache",
"logger",
"http",
"auth",
"cache",
"crypto",
"parser",
"parser",
"auth",
"session",
"runtime",
"engine",
],
}
)
axis_angles = {"core": 90, "utility": 210, "interface": 330}
axis_colors = {"core": BRAND, "utility": COLOR_2, "interface": COLOR_3}
max_degree = nodes["degree"].max()
nodes_by_category = {}
for cat in ["core", "utility", "interface"]:
cat_nodes = nodes[nodes["category"] == cat].sort_values("degree", ascending=False).reset_index(drop=True)
nodes_by_category[cat] = cat_nodes
positions = []
for cat, cat_nodes in nodes_by_category.items():
angle_deg = axis_angles[cat]
angle_rad = np.radians(angle_deg)
for _idx, row in cat_nodes.iterrows():
base_radius = 0.25 + (row["degree"] / max_degree) * 0.70
x = base_radius * np.cos(angle_rad)
y = base_radius * np.sin(angle_rad)
node_size = 6 + (row["degree"] / max_degree) * 16
positions.append(
{
"id": row["id"],
"x": x,
"y": y,
"category": row["category"],
"degree": row["degree"],
"node_size": node_size,
}
)
node_positions = pd.DataFrame(positions)
axis_lines = []
for cat, angle in axis_angles.items():
angle_rad = np.radians(angle)
axis_lines.append(
{"x": 0, "y": 0, "xend": 1.0 * np.cos(angle_rad), "yend": 1.0 * np.sin(angle_rad), "category": cat}
)
axis_df = pd.DataFrame(axis_lines)
edge_data = []
for _, row in edges.iterrows():
src_match = node_positions[node_positions["id"] == row["source"]]
tgt_match = node_positions[node_positions["id"] == row["target"]]
if len(src_match) == 0 or len(tgt_match) == 0:
continue
src_pos = src_match.iloc[0]
tgt_pos = tgt_match.iloc[0]
src_cat = src_pos["category"]
tgt_cat = tgt_pos["category"]
if src_cat == tgt_cat:
mid_factor = 0.3
else:
mid_factor = 0.15
ctrl_x = mid_factor * (src_pos["x"] + tgt_pos["x"]) / 2
ctrl_y = mid_factor * (src_pos["y"] + tgt_pos["y"]) / 2
n_points = 20
for i in range(n_points):
t0 = i / n_points
t1 = (i + 1) / n_points
x0 = (1 - t0) ** 2 * src_pos["x"] + 2 * (1 - t0) * t0 * ctrl_x + t0**2 * tgt_pos["x"]
y0 = (1 - t0) ** 2 * src_pos["y"] + 2 * (1 - t0) * t0 * ctrl_y + t0**2 * tgt_pos["y"]
x1 = (1 - t1) ** 2 * src_pos["x"] + 2 * (1 - t1) * t1 * ctrl_x + t1**2 * tgt_pos["x"]
y1 = (1 - t1) ** 2 * src_pos["y"] + 2 * (1 - t1) * t1 * ctrl_y + t1**2 * tgt_pos["y"]
edge_data.append(
{
"x": x0,
"y": y0,
"xend": x1,
"yend": y1,
"src_cat": src_cat,
"tgt_cat": tgt_cat,
"edge_color": axis_colors[src_cat],
}
)
edge_df = pd.DataFrame(edge_data)
axis_labels = []
for cat, angle in axis_angles.items():
angle_rad = np.radians(angle)
axis_labels.append(
{"x": 1.18 * np.cos(angle_rad), "y": 1.18 * np.sin(angle_rad), "label": cat.upper(), "category": cat}
)
label_df = pd.DataFrame(axis_labels)
node_labels = []
for _, row in node_positions.iterrows():
angle_deg = axis_angles[row["category"]]
angle_rad = np.radians(angle_deg)
offset = 0.18
perp_angle = angle_rad + np.pi / 2
label_x = row["x"] + offset * np.cos(perp_angle)
label_y = row["y"] + offset * np.sin(perp_angle)
node_labels.append({"x": label_x, "y": label_y, "label": row["id"], "category": row["category"]})
node_labels_df = pd.DataFrame(node_labels)
plot = (
ggplot()
+ geom_segment(aes(x="x", y="y", xend="xend", yend="yend", color="src_cat"), data=edge_df, size=1.5, alpha=0.5)
+ geom_segment(aes(x="x", y="y", xend="xend", yend="yend", color="category"), data=axis_df, size=3.5, alpha=0.8)
+ geom_point(aes(x="x", y="y", color="category", size="node_size"), data=node_positions, alpha=0.95)
+ geom_text(aes(x="x", y="y", label="label"), data=node_labels_df, size=10, color=INK)
+ geom_text(aes(x="x", y="y", label="label", color="category"), data=label_df, size=18)
+ scale_color_manual(values=axis_colors)
+ coord_fixed(ratio=1)
+ xlim(-1.6, 1.6)
+ ylim(-1.6, 1.6)
+ labs(title="hive-basic · plotnine · anyplot.ai")
+ theme_void()
+ theme(
figure_size=(16, 16),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
plot_title=element_text(size=28, color=INK, weight="bold"),
legend_position="none",
plot_margin=0.01,
)
)
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)
Part of Basic Hive Plot on anyplot.ai.