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: ggplot2 3.5.1 | R 4.4.1
#' Quality: 86/100 | Created: 2026-05-17
library(ggplot2)
library(dplyr)
library(ragg)
set.seed(42)
# Theme tokens
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
IMPRINT <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477")
# Data: Trade network between countries (weighted by annual trade volume)
set.seed(42)
nodes <- data.frame(
id = 1:15,
country = c("USA", "China", "India", "Japan", "Germany",
"UK", "France", "Brazil", "Canada", "Mexico",
"South Korea", "Italy", "Spain", "Netherlands", "Australia"),
region = c("Americas", "Asia", "Asia", "Asia", "Europe",
"Europe", "Europe", "Americas", "Americas", "Americas",
"Asia", "Europe", "Europe", "Europe", "Oceania")
)
# Edges: trade relationships with weights (annual volume in billions USD)
edges <- data.frame(
source = c(1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5, 5, 6, 6, 7, 8, 9, 10, 11, 12, 13, 14),
target = c(2, 3, 4, 3, 5, 6, 4, 5, 5, 11, 12, 6, 7, 7, 14, 8, 9, 10, 9, 2, 13, 14, 1),
weight = c(640, 380, 285, 620, 290, 240, 185, 165, 310, 85, 150, 520, 380, 275, 220, 185, 95, 120, 85, 330, 95, 80, 65)
)
# Simple spring-like layout: place nodes on a circle with slight random jitter
n_nodes <- nrow(nodes)
angles <- seq(0, 2 * pi, length.out = n_nodes + 1)[1:n_nodes]
nodes$x <- 2 * cos(angles) + rnorm(n_nodes, 0, 0.3)
nodes$y <- 2 * sin(angles) + rnorm(n_nodes, 0, 0.3)
# Create edge data frame with node positions
edges_positioned <- edges %>%
left_join(nodes %>% select(id, x, y), by = c("source" = "id")) %>%
rename(x_start = x, y_start = y) %>%
left_join(nodes %>% select(id, x, y), by = c("target" = "id")) %>%
rename(x_end = x, y_end = y)
# Normalize edge weights for linewidth (1 to 4)
weight_min <- min(edges_positioned$weight)
weight_max <- max(edges_positioned$weight)
edges_positioned$linewidth <- 1 + 3 * (edges_positioned$weight - weight_min) / (weight_max - weight_min)
# Create base theme
anyplot_theme <- theme_minimal(base_size = 14) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = NA),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid = element_blank(),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(color = INK, size = 24, face = "bold"),
plot.margin = margin(20, 20, 20, 20, "pt")
)
# Create plot
p <- ggplot() +
# Draw edges first (so they appear behind nodes)
geom_segment(
data = edges_positioned,
aes(x = x_start, y = y_start, xend = x_end, yend = y_end, linewidth = linewidth),
color = IMPRINT[1],
alpha = 0.5,
lineend = "round"
) +
# Draw nodes
geom_point(
data = nodes,
aes(x = x, y = y),
color = IMPRINT[1],
size = 8,
alpha = 0.9
) +
# Node labels
geom_text(
data = nodes,
aes(x = x, y = y, label = substr(country, 1, 3)),
color = PAGE_BG,
size = 3.5,
fontface = "bold"
) +
scale_linewidth(range = c(1, 4), guide = "none") +
coord_equal() +
labs(title = "network-weighted · ggplot2 · anyplot.ai") +
anyplot_theme
# Save
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 16,
height = 9,
units = "in",
dpi = 300
)
Part of Weighted Network Graph with Edge Thickness on anyplot.ai.