A weighted network graph displays relationships between entities using edge thickness to represent connection strength or weight. Unlike basic network graphs where edges are uniform, the varying line widths immediately communicate the relative importance of each relationship. This visualization makes it easy to identify strong vs weak connections, central hubs with many heavy links, and structural patterns in weighted relational data.
""" anyplot.ai
network-weighted: Weighted Network Graph with Edge Thickness
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-17
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, LabelSet, Range1d
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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"
BRAND = "#009E73"
# Data: Trade network between 15 countries (billions USD annual trade volume)
np.random.seed(42)
# Define nodes (15 countries/regions)
node_labels = [
"USA",
"China",
"Germany",
"Japan",
"UK",
"France",
"Canada",
"Mexico",
"Brazil",
"India",
"S. Korea",
"Australia",
"Singapore",
"Netherlands",
"Switzerland",
]
n_nodes = len(node_labels)
# Generate weighted edges (trade relationships)
edges = [
# USA trade partners
(0, 1, 580),
(0, 2, 180),
(0, 3, 220),
(0, 4, 130),
(0, 6, 620),
(0, 7, 680),
(0, 10, 170),
# China trade partners
(1, 3, 340),
(1, 10, 290),
(1, 2, 220),
(1, 11, 180),
(1, 12, 120),
(1, 9, 110),
# European connections
(2, 4, 160),
(2, 5, 180),
(2, 13, 200),
(2, 14, 140),
(4, 5, 110),
(5, 13, 90),
# Asian connections
(3, 10, 85),
(9, 12, 55),
(12, 11, 70),
# Americas
(6, 7, 80),
(0, 8, 95),
(8, 9, 40),
]
# Use force-directed layout for node positions
positions = np.random.rand(n_nodes, 2) * 10
for _ in range(100):
forces = np.zeros((n_nodes, 2))
# Repulsion between all nodes
for i in range(n_nodes):
for j in range(i + 1, n_nodes):
diff = positions[i] - positions[j]
dist = np.linalg.norm(diff) + 0.1
force = diff / (dist**2) * 2
forces[i] += force
forces[j] -= force
# Attraction along edges (weighted)
for src, tgt, weight in edges:
diff = positions[tgt] - positions[src]
dist = np.linalg.norm(diff) + 0.1
force = diff * 0.01 * (weight / 200)
forces[src] += force
forces[tgt] -= force
positions += forces * 0.1
# Center and scale positions
positions -= positions.mean(axis=0)
positions /= positions.max() * 1.2
node_x = positions[:, 0]
node_y = positions[:, 1]
# Calculate weighted degree for node sizing
weighted_degree = np.zeros(n_nodes)
for src, tgt, weight in edges:
weighted_degree[src] += weight
weighted_degree[tgt] += weight
# Normalize node sizes
min_size = 30
max_size = 80
node_sizes = min_size + (weighted_degree - weighted_degree.min()) / (
weighted_degree.max() - weighted_degree.min() + 0.1
) * (max_size - min_size)
# Prepare edge data
edge_x0, edge_y0, edge_x1, edge_y1 = [], [], [], []
edge_widths = []
# Normalize edge weights to line widths
max_weight = max(e[2] for e in edges)
min_weight = min(e[2] for e in edges)
for src, tgt, weight in edges:
edge_x0.append(node_x[src])
edge_y0.append(node_y[src])
edge_x1.append(node_x[tgt])
edge_y1.append(node_y[tgt])
# Scale width: thinnest = 2, thickest = 20
normalized = (weight - min_weight) / (max_weight - min_weight + 0.1)
edge_widths.append(2 + normalized * 18)
# Create figure
p = figure(
width=4800,
height=2700,
title="network-weighted · bokeh · anyplot.ai",
x_axis_label="",
y_axis_label="",
tools="",
toolbar_location=None,
)
# Remove axes and grid
p.xaxis.visible = False
p.yaxis.visible = False
p.xgrid.visible = False
p.ygrid.visible = False
p.outline_line_color = None
# Theme-adaptive styling
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.title.text_color = INK
p.title.text_font_size = "28pt"
p.title.align = "center"
# Set range with padding
padding = 0.15
p.x_range = Range1d(node_x.min() - padding, node_x.max() + padding)
p.y_range = Range1d(node_y.min() - padding, node_y.max() + padding)
# Draw edges with weighted line widths
for i in range(len(edge_x0)):
normalized = (edge_widths[i] - 2) / 18
alpha = 0.3 + normalized * 0.5
p.segment(
x0=[edge_x0[i]],
y0=[edge_y0[i]],
x1=[edge_x1[i]],
y1=[edge_y1[i]],
line_width=edge_widths[i],
line_color=BRAND,
line_alpha=alpha,
line_cap="round",
)
# Create node source with weighted degree
node_source = ColumnDataSource(
data={
"x": node_x,
"y": node_y,
"size": node_sizes,
"labels": node_labels,
"weighted_degree": [f"{int(wd):,}" for wd in weighted_degree],
}
)
# Draw nodes
nodes_renderer = p.scatter(
x="x", y="y", source=node_source, size="size", fill_color=BRAND, line_color=INK_SOFT, line_width=3, fill_alpha=0.85
)
# Add hover tool for interactivity
hover = HoverTool(
renderers=[nodes_renderer], tooltips=[("Country", "@labels"), ("Total Trade (B USD)", "@weighted_degree")]
)
p.add_tools(hover)
# Add node labels
labels = LabelSet(
x="x",
y="y",
text="labels",
source=node_source,
text_font_size="16pt",
text_align="center",
text_baseline="middle",
text_color=INK,
text_font_style="bold",
)
p.add_layout(labels)
# Add legend annotation for edge thickness
legend_x = node_x.min() - padding + 0.03
legend_y = node_y.max() + padding - 0.02
# Legend title
p.text(
x=[legend_x],
y=[legend_y],
text=["Trade Volume (B USD)"],
text_font_size="22pt",
text_font_style="bold",
text_color=INK,
)
# Legend lines showing weight scale
legend_weights = [min_weight, (min_weight + max_weight) / 2, max_weight]
legend_labels = [f"{int(w)} B" for w in legend_weights]
legend_widths = [4, 14, 26]
for i, (lw, label) in enumerate(zip(legend_widths, legend_labels, strict=True)):
y_pos = legend_y - 0.055 - i * 0.05
normalized = (lw - 2) / 18
alpha = 0.3 + normalized * 0.5
p.segment(
x0=[legend_x],
y0=[y_pos],
x1=[legend_x + 0.12],
y1=[y_pos],
line_width=lw,
line_color=BRAND,
line_alpha=alpha,
line_cap="round",
)
p.text(
x=[legend_x + 0.14], y=[y_pos], text=[label], text_font_size="18pt", text_baseline="middle", text_color=INK_SOFT
)
# Save HTML (interactive artifact)
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome for PNG
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 Weighted Network Graph with Edge Thickness on anyplot.ai.