diff --git a/plots/network-weighted/implementations/plotly.py b/plots/network-weighted/implementations/plotly.py
new file mode 100644
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+++ b/plots/network-weighted/implementations/plotly.py
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+""" pyplots.ai
+network-weighted: Weighted Network Graph with Edge Thickness
+Library: plotly 6.5.1 | Python 3.13.11
+Quality: 92/100 | Created: 2026-01-08
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data - Trade network between countries (billions USD)
+np.random.seed(42)
+
+# Define nodes (countries)
+countries = [
+    "USA",
+    "China",
+    "Germany",
+    "Japan",
+    "UK",
+    "France",
+    "Canada",
+    "Mexico",
+    "Brazil",
+    "India",
+    "Australia",
+    "S. Korea",
+    "Netherlands",
+    "Italy",
+    "Spain",
+]
+n_nodes = len(countries)
+node_idx = {name: i for i, name in enumerate(countries)}
+
+# Create weighted edges (trade relationships)
+edges = [
+    # Major trade routes (high weight)
+    ("USA", "China", 580),
+    ("USA", "Canada", 620),
+    ("USA", "Mexico", 550),
+    ("China", "Japan", 320),
+    ("China", "S. Korea", 280),
+    ("China", "Germany", 190),
+    ("Germany", "France", 180),
+    ("Germany", "Netherlands", 210),
+    ("Germany", "Italy", 140),
+    ("UK", "Germany", 130),
+    ("UK", "USA", 140),
+    ("UK", "Netherlands", 90),
+    ("Japan", "USA", 200),
+    ("Japan", "S. Korea", 85),
+    # Medium trade routes
+    ("France", "Italy", 95),
+    ("France", "Spain", 110),
+    ("Spain", "Italy", 50),
+    ("Canada", "Mexico", 40),
+    ("Brazil", "USA", 75),
+    ("Brazil", "China", 100),
+    ("India", "USA", 90),
+    ("India", "China", 115),
+    ("India", "UK", 35),
+    ("Australia", "China", 145),
+    ("Australia", "Japan", 55),
+    ("Australia", "S. Korea", 45),
+    # Lower trade routes
+    ("Netherlands", "UK", 65),
+    ("S. Korea", "USA", 120),
+    ("Mexico", "China", 70),
+]
+
+# Compute force-directed layout (Fruchterman-Reingold algorithm)
+pos = np.random.rand(n_nodes, 2) * 2 - 1
+k = 0.5
+for _ in range(200):
+    displacement = np.zeros((n_nodes, 2))
+    # Repulsive forces
+    for i in range(n_nodes):
+        diff = pos[i] - pos
+        dist = np.sqrt((diff**2).sum(axis=1))
+        dist = np.where(dist < 0.01, 0.01, dist)
+        rep_force = k**2 / dist
+        rep_force[i] = 0
+        displacement[i] += (diff * rep_force[:, np.newaxis]).sum(axis=0)
+    # Attractive forces along edges
+    for source, target, weight in edges:
+        i, j = node_idx[source], node_idx[target]
+        diff = pos[j] - pos[i]
+        dist = np.sqrt((diff**2).sum())
+        if dist > 0.01:
+            attr_force = dist**2 / k * (1 + weight / 200)
+            displacement[i] += diff / dist * attr_force
+            displacement[j] -= diff / dist * attr_force
+    # Update positions
+    length = np.sqrt((displacement**2).sum(axis=1))
+    length = np.where(length < 0.01, 0.01, length)
+    pos += displacement / length[:, np.newaxis] * min(0.1, k)
+
+# Normalize positions with margin for labels and annotation
+pos = (pos - pos.min(axis=0)) / (pos.max(axis=0) - pos.min(axis=0))
+pos = pos * 1.6 - 0.8  # Scale to [-0.8, 0.8]
+# Center the layout
+pos = pos - pos.mean(axis=0)
+node_positions = {countries[i]: pos[i] for i in range(n_nodes)}
+
+# Calculate weighted degree for node sizing
+weighted_degree = dict.fromkeys(countries, 0)
+for source, target, weight in edges:
+    weighted_degree[source] += weight
+    weighted_degree[target] += weight
+
+node_sizes = [20 + (weighted_degree[node] / 40) for node in countries]
+
+# Create edge traces with varying thickness
+edge_traces = []
+min_weight = min(w for _, _, w in edges)
+max_weight = max(w for _, _, w in edges)
+
+for source, target, weight in edges:
+    x0, y0 = node_positions[source]
+    x1, y1 = node_positions[target]
+    # Scale width: 2 to 18 based on weight
+    normalized = (weight - min_weight) / (max_weight - min_weight)
+    line_width = 2 + normalized * 16
+    # Color alpha based on weight (darker = stronger)
+    alpha = 0.4 + normalized * 0.5
+    edge_traces.append(
+        go.Scatter(
+            x=[x0, x1, None],
+            y=[y0, y1, None],
+            mode="lines",
+            line={"width": line_width, "color": f"rgba(48, 105, 152, {alpha})"},
+            hoverinfo="text",
+            text=f"{source} ↔ {target}: ${weight}B",
+            showlegend=False,
+        )
+    )
+
+# Create node trace
+node_x = [node_positions[node][0] for node in countries]
+node_y = [node_positions[node][1] for node in countries]
+
+# Calculate smart label positions to avoid overlap with explicit handling
+# for known problematic pairs: Japan/S.Korea and Italy/France
+label_positions = []
+
+for i, node in enumerate(countries):
+    x, y = node_positions[node]
+    # Find nearby nodes and adjust position
+    nearby_above = 0
+    nearby_below = 0
+    nearby_left = 0
+    nearby_right = 0
+    for j, other in enumerate(countries):
+        if i != j:
+            ox, oy = node_positions[other]
+            dx, dy = x - ox, y - oy
+            dist = np.sqrt(dx**2 + dy**2)
+            if dist < 0.35:
+                if dy > 0:
+                    nearby_below += 1
+                else:
+                    nearby_above += 1
+                if dx > 0:
+                    nearby_left += 1
+                else:
+                    nearby_right += 1
+
+    # Handle specific known close pairs to avoid overlap
+    if node == "Japan":
+        pos_choice = "top right"
+    elif node == "S. Korea":
+        pos_choice = "bottom left"
+    elif node == "Italy":
+        pos_choice = "top left"
+    elif node == "France":
+        pos_choice = "bottom right"
+    elif nearby_above > nearby_below:
+        pos_choice = "bottom center"
+    elif nearby_left > nearby_right:
+        pos_choice = "middle right"
+    elif nearby_right > nearby_left:
+        pos_choice = "middle left"
+    else:
+        pos_choice = "top center"
+    label_positions.append(pos_choice)
+
+node_trace = go.Scatter(
+    x=node_x,
+    y=node_y,
+    mode="markers+text",
+    marker={"size": node_sizes, "color": "#FFD43B", "line": {"width": 2, "color": "#306998"}},
+    text=countries,
+    textposition=label_positions,
+    textfont={"size": 16, "color": "#333333"},
+    hoverinfo="text",
+    hovertext=[f"{c}<br>Trade Volume: ${weighted_degree[c]}B" for c in countries],
+    showlegend=False,
+)
+
+# Create figure
+fig = go.Figure()
+
+# Add edges first (behind nodes)
+for trace in edge_traces:
+    fig.add_trace(trace)
+
+# Add nodes
+fig.add_trace(node_trace)
+
+# Add weight scale annotation (positioned at top-left to avoid cutoff)
+fig.add_annotation(
+    x=0.01,
+    y=0.99,
+    xref="paper",
+    yref="paper",
+    text="Edge Thickness = Trade Volume<br>35B USD (thin) to 620B USD (thick)",
+    showarrow=False,
+    font={"size": 18, "color": "#333333", "family": "Arial"},
+    align="left",
+    xanchor="left",
+    yanchor="top",
+    bgcolor="rgba(255,255,255,0.95)",
+    bordercolor="#999999",
+    borderwidth=1,
+    borderpad=10,
+)
+
+# Update layout
+fig.update_layout(
+    title={
+        "text": "network-weighted · plotly · pyplots.ai", "font": {"size": 28, "color": "#333333"}, "x": 0.5, "xanchor": "center"
+    },
+    xaxis={"showgrid": False, "zeroline": False, "showticklabels": False, "title": ""},
+    yaxis={"showgrid": False, "zeroline": False, "showticklabels": False, "title": ""},
+    template="plotly_white",
+    showlegend=False,
+    margin={"l": 80, "r": 80, "t": 100, "b": 80},
+    plot_bgcolor="white",
+)
+
+# Save outputs
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html")
diff --git a/plots/network-weighted/metadata/plotly.yaml b/plots/network-weighted/metadata/plotly.yaml
new file mode 100644
index 0000000000..03662044a7
--- /dev/null
+++ b/plots/network-weighted/metadata/plotly.yaml
@@ -0,0 +1,222 @@
+library: plotly
+specification_id: network-weighted
+created: '2026-01-08T15:58:57Z'
+updated: '2026-01-08T16:26:11Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20822849491
+issue: 3290
+python_version: 3.13.11
+library_version: 6.5.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/network-weighted/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/network-weighted/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/network-weighted/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Clear edge thickness differentiation showing varying trade volumes from thin (35B)
+    to thick (620B USD)
+  - Well-implemented force-directed layout with weight-influenced attraction keeping
+    high-trade partners close (USA-China-Canada cluster)
+  - Effective node sizing based on weighted degree - USA and China correctly appear
+    as the largest hubs
+  - Smart label positioning avoids overlap through explicit handling of problematic
+    pairs (Japan/S.Korea, Italy/France)
+  - Informative weight scale annotation box explaining edge thickness interpretation
+  - Clean data representing a realistic international trade network scenario
+  weaknesses:
+  - The network is positioned more toward the right side of the canvas, leaving significant
+    whitespace on the left - better centering would improve layout balance
+  image_description: |-
+    The plot displays a weighted network graph of international trade relationships between 15 countries. Nodes are represented as yellow circular markers with dark blue (#306998) borders, sized proportionally to their weighted degree (total trade volume). USA and China appear as the largest nodes, reflecting their position as major trade hubs.
+
+    Edges connect trading partners with varying thickness representing trade volume in billions of USD. The thickest edges (dark blue, high opacity) connect USA-Canada (~620B), USA-Mexico (~550B), and USA-China (~580B), clearly visible as prominent thick lines. Thinner, more transparent edges represent smaller trade relationships like Spain-Italy (50B) and India-UK (35B).
+
+    The title "network-weighted · plotly · pyplots.ai" is centered at the top in dark gray text (size 28). A legend box in the top-left corner explains "Edge Thickness = Trade Volume" with the range "35B USD (thin) to 620B USD (thick)" on a white background with subtle border.
+
+    Country labels are displayed in dark gray (#333333) with size 16 font, positioned intelligently around each node to avoid overlap - Japan appears "top right", S. Korea "bottom left", Italy "top left", and France "bottom right" with other nodes using contextual positioning based on nearby neighbors.
+
+    The layout uses a force-directed algorithm where high-trade partners are pulled closer together, creating a visible USA-China-Mexico-Canada cluster on the right side. European countries (Germany, France, Italy, Spain, Netherlands, UK) form a cluster on the left-center. Asian-Pacific countries (Japan, S. Korea, Australia) connect primarily through China.
+
+    Background is clean white with no grid lines (appropriate for network graphs). The overall layout fills approximately 60-70% of the canvas with the network slightly positioned toward the right.
+  criteria_checklist:
+    visual_quality:
+      score: 36
+      max: 40
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 10
+        max: 10
+        passed: true
+        comment: Title at 28pt, node labels at 16pt, annotation text at 18pt - all
+          clearly readable at 4800x2700
+      - id: VQ-02
+        name: No Overlap
+        score: 8
+        max: 8
+        passed: true
+        comment: Smart label positioning with explicit handling for close pairs (Japan/S.Korea,
+          Italy/France) prevents all text overlap
+      - id: VQ-03
+        name: Element Visibility
+        score: 8
+        max: 8
+        passed: true
+        comment: Node sizes (20-55px) appropriately scaled by weighted degree; edge
+          widths (2-18px) provide clear visual differentiation
+      - id: VQ-04
+        name: Color Accessibility
+        score: 5
+        max: 5
+        passed: true
+        comment: Single blue color scheme for edges with alpha variation (0.4-0.9);
+          yellow nodes with blue borders - no colorblind issues
+      - id: VQ-05
+        name: Layout Balance
+        score: 3
+        max: 5
+        passed: true
+        comment: Network fills ~60-70% of canvas but positioned slightly right of
+          center; left side has more whitespace
+      - id: VQ-06
+        name: Axis Labels
+        score: 0
+        max: 2
+        passed: true
+        comment: N/A for network graph - axes correctly hidden (no labels needed)
+      - id: VQ-07
+        name: Grid & Legend
+        score: 2
+        max: 2
+        passed: true
+        comment: No grid (appropriate for network); annotation box with weight scale
+          well-positioned in top-left with clear styling
+    spec_compliance:
+      score: 25
+      max: 25
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 8
+        max: 8
+        passed: true
+        comment: Correct weighted network graph with edge thickness mapping
+      - id: SC-02
+        name: Data Mapping
+        score: 5
+        max: 5
+        passed: true
+        comment: Nodes=countries, edges=trade relationships, weight=trade volume correctly
+          mapped to edge thickness
+      - id: SC-03
+        name: Required Features
+        score: 5
+        max: 5
+        passed: true
+        comment: 'All spec features: edge thickness for weight, node sizing by weighted
+          degree, weight scale annotation, force-directed layout with weight influence'
+      - id: SC-04
+        name: Data Range
+        score: 3
+        max: 3
+        passed: true
+        comment: All 15 nodes and 29 edges visible within plot bounds
+      - id: SC-05
+        name: Legend Accuracy
+        score: 2
+        max: 2
+        passed: true
+        comment: Weight scale annotation accurately describes edge thickness range
+          (35B-620B USD)
+      - id: SC-06
+        name: Title Format
+        score: 2
+        max: 2
+        passed: true
+        comment: 'Uses exact format: network-weighted · plotly · pyplots.ai'
+    data_quality:
+      score: 20
+      max: 20
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 8
+        max: 8
+        passed: true
+        comment: 'Shows full range: high-weight edges (USA-Canada 620B), medium (France-Spain
+          110B), low (India-UK 35B); large hubs (USA, China) vs peripheral nodes (Spain,
+          Australia)'
+      - id: DQ-02
+        name: Realistic Context
+        score: 7
+        max: 7
+        passed: true
+        comment: International trade network is real-world relevant; trade volumes
+          approximate actual relationships (USA-China, USA-Canada top partners)
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 5
+        max: 5
+        passed: true
+        comment: Trade values in billions USD (35B-620B) are realistic for major bilateral
+          trade flows
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: Follows imports → data → layout computation → edge traces → node
+          trace → figure → save structure; no functions/classes
+      - id: CQ-02
+        name: Reproducibility
+        score: 3
+        max: 3
+        passed: true
+        comment: np.random.seed(42) ensures reproducible force-directed layout
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: Only numpy and plotly.graph_objects imported, both used
+      - id: CQ-04
+        name: No Deprecated API
+        score: 1
+        max: 1
+        passed: true
+        comment: Uses current Plotly 6.x API
+      - id: CQ-05
+        name: Output Correct
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png and plot.html
+    library_features:
+      score: 5
+      max: 5
+      items:
+      - id: LF-01
+        name: Distinctive Features
+        score: 5
+        max: 5
+        passed: true
+        comment: 'Uses Plotly-specific features: hover tooltips with formatted text
+          (trade pairs + values), individual edge traces for per-edge styling, annotations
+          with paper coordinates, HTML export for interactivity'
+  verdict: APPROVED
+impl_tags:
+  dependencies: []
+  techniques:
+  - annotations
+  - hover-tooltips
+  - html-export
+  patterns:
+  - data-generation
+  - iteration-over-groups
+  dataprep: []
+  styling:
+  - alpha-blending