graph-layout 0.1.8

A collection of graph layout algorithms in Python

graph-layout - Python Graph Layout Library

A collection of graph layout algorithms in Python.

Layout Algorithms

Family	Algorithm	Description
Basic	RandomLayout	Random positions within canvas (baseline/starting point)
Bipartite	BipartiteLayout	Two parallel rows for bipartite graphs
Cola	Layout	Constraint-based layout with overlap avoidance (port of WebCola)
Force-Directed	ForceAtlas2Layout	Continuous layout with adaptive speeds (Gephi algorithm)
	FruchtermanReingoldLayout	Classic force-directed with cooling temperature
	KamadaKawaiLayout	Stress minimization based on graph-theoretic distances
	SpringLayout	Simple Hooke's law spring forces
	YifanHuLayout	Multilevel force-directed for medium-large graphs
Hierarchical	SugiyamaLayout	Layered DAG drawing (Sugiyama method)
	ReingoldTilfordLayout	Classic tree layout
	RadialTreeLayout	Radial tree with root at center
Circular	CircularLayout	Nodes arranged on a circle
	ShellLayout	Concentric circles by degree or grouping
Spectral	SpectralLayout	Laplacian eigenvector embedding
Orthogonal	KandinskyLayout	Edges use only horizontal/vertical segments
	GIOTTOLayout	Bend-optimal for degree-4 planar graphs

Installation

# Standard installation (includes Cython extensions for best performance)
pip install graph-layout

# With ILP compaction support (for optimal Kandinsky area minimization)
pip install graph-layout[ilp]

# Development installation
git clone https://github.com/shakfu/graph-layout.git
cd graph-layout
uv sync

Quick Start

Random Layout (Baseline)

Random layout places nodes at random positions. Useful as a baseline for comparing layout quality or as a starting point for iterative algorithms:

from graph_layout import RandomLayout

nodes = [{} for _ in range(10)]
links = [{'source': i, 'target': (i + 1) % 10} for i in range(10)]

layout = RandomLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    margin=50,        # Optional padding from edges
    random_seed=42,   # For reproducible layouts
)
layout.run()

for i, node in enumerate(layout.nodes):
    print(f"Node {i}: ({node.x:.1f}, {node.y:.1f})")

Force-Directed Layout

from graph_layout import FruchtermanReingoldLayout

nodes = [{} for _ in range(6)]
links = [
    {'source': 0, 'target': 1},
    {'source': 1, 'target': 2},
    {'source': 2, 'target': 0},
    {'source': 3, 'target': 4},
    {'source': 4, 'target': 5},
    {'source': 2, 'target': 3},
]

layout = FruchtermanReingoldLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    iterations=300,
)
layout.run()

for i, node in enumerate(layout.nodes):
    print(f"Node {i}: ({node.x:.1f}, {node.y:.1f})")

ForceAtlas2 Layout

ForceAtlas2 is designed for large network visualization with degree-weighted repulsion and adaptive speeds:

from graph_layout import ForceAtlas2Layout

layout = ForceAtlas2Layout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    scaling=2.0,           # Repulsion strength
    gravity=1.0,           # Pull toward center
    linlog_mode=True,      # Tighter clusters
    strong_gravity_mode=False,  # Distance-based gravity
)
layout.run()

Yifan Hu Multilevel Layout

Yifan Hu is ideal for medium-large graphs (1K-100K nodes) using multilevel coarsening:

from graph_layout import YifanHuLayout

layout = YifanHuLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    use_barnes_hut=True,       # O(n log n) approximation
    coarsening_threshold=0.75, # Stop coarsening when ratio > 0.75
    min_coarsest_size=10,      # Minimum nodes in coarsest graph
)
layout.run()

Cola (Constraint-Based) Layout

from graph_layout import ColaLayoutAdapter

nodes = [
    {'x': 0, 'y': 0, 'width': 50, 'height': 30},
    {'x': 100, 'y': 0, 'width': 50, 'height': 30},
    {'x': 200, 'y': 0, 'width': 50, 'height': 30},
]
links = [
    {'source': 0, 'target': 1},
    {'source': 1, 'target': 2},
]

layout = ColaLayoutAdapter(
    nodes=nodes,
    links=links,
    avoid_overlaps=True,
    link_distance=100,
)
layout.run()

Hierarchical Layout (Trees/DAGs)

from graph_layout import SugiyamaLayout

# Tree structure
nodes = [{} for _ in range(7)]
links = [
    {'source': 0, 'target': 1},
    {'source': 0, 'target': 2},
    {'source': 1, 'target': 3},
    {'source': 1, 'target': 4},
    {'source': 2, 'target': 5},
    {'source': 2, 'target': 6},
]

layout = SugiyamaLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    layer_separation=80,
    node_separation=50,
)
layout.run()

Circular Layout

from graph_layout import CircularLayout, ShellLayout

nodes = [{} for _ in range(10)]
links = [{'source': i, 'target': (i + 1) % 10} for i in range(10)]

# Simple circular
layout = CircularLayout(nodes=nodes, links=links, size=(800, 600))
layout.run()

# Concentric shells by degree
layout = ShellLayout(nodes=nodes, links=links, size=(800, 600), auto_shells=2)
layout.run()

Spectral Layout

from graph_layout import SpectralLayout

layout = SpectralLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    normalized=True,
)
layout.run()

Bipartite Layout

Bipartite layout places nodes in two parallel rows, ideal for user-item networks, author-paper relationships, or any bipartite graph:

from graph_layout import BipartiteLayout

# User-item bipartite graph
nodes = [{} for _ in range(7)]  # 3 users + 4 items
links = [
    {'source': 0, 'target': 3},  # user 0 -> item 3
    {'source': 0, 'target': 4},
    {'source': 1, 'target': 4},
    {'source': 1, 'target': 5},
    {'source': 2, 'target': 5},
    {'source': 2, 'target': 6},
]

layout = BipartiteLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    top_set=[0, 1, 2],       # Users on top row
    bottom_set=[3, 4, 5, 6], # Items on bottom row
    minimize_crossings=True, # Reorder to reduce edge crossings
)
layout.run()

# Check if graph is bipartite
print(f"Is bipartite: {layout.is_bipartite}")

# Count edge crossings (O(m log m) using inversion counting)
from graph_layout.bipartite import count_crossings
edges = [(0, 3), (0, 4), (1, 4), (1, 5), (2, 5), (2, 6)]  # Same as links above
crossings = count_crossings(layout.top_nodes, layout.bottom_nodes, edges)
print(f"Edge crossings: {crossings}")

Algorithm insight: Edge crossings in a bipartite drawing equal the number of inversions when edges are sorted by their top-layer position. This allows O(m log m) counting via merge sort instead of O(m²) pairwise comparison—a 188x speedup for 10,000 edges.

Orthogonal Layout (Kandinsky)

Kandinsky layout produces diagrams where all edges use only horizontal and vertical segments. Ideal for UML diagrams, flowcharts, and ER diagrams. Uses a TSM (Topology-Shape-Metrics) approach:

from graph_layout import KandinskyLayout

layout = KandinskyLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    node_width=60,
    node_height=40,
    node_separation=60,
    handle_crossings=True,   # Insert crossing vertices for non-planar graphs
    optimize_bends=True,     # Minimize bends using min-cost flow
    compact=True,            # Compact layout to reduce area
    compaction_method="auto", # "auto", "greedy", or "ilp" (ILP requires scipy)
)
layout.run()

# Access edge routing information
for edge in layout.orthogonal_edges:
    print(f"Edge {edge.source}->{edge.target}: {len(edge.bends)} bends")

# Check crossing information
print(f"Edge crossings detected: {layout.num_crossings}")
print(f"Total bends: {layout.total_bends}")

Port Constraints

Specify which side of a node edges should exit/enter from:

from graph_layout import KandinskyLayout
from graph_layout.orthogonal import Side

# Links with explicit port constraints
links = [
    {"source": 0, "target": 1, "source_side": Side.EAST, "target_side": Side.WEST},
    {"source": 1, "target": 2, "source_side": "south", "target_side": "north"},  # Strings work too
    {"source": 2, "target": 3},  # No constraint - uses heuristic
]

layout = KandinskyLayout(nodes=nodes, links=links, size=(800, 600))
layout.run()

# Verify constraints were applied
edge = layout.orthogonal_edges[0]
print(f"Edge exits from: {edge.source_port.side}")  # Side.EAST

GIOTTO Layout (Degree-4 Planar)

GIOTTO produces bend-optimal orthogonal drawings for planar graphs where every node has at most 4 edges (degree <= 4). Based on Tamassia's algorithm:

from graph_layout import GIOTTOLayout

# 3x3 grid graph (degree-4 planar)
nodes = [{} for _ in range(9)]
links = [
    # Horizontal edges
    {"source": 0, "target": 1}, {"source": 1, "target": 2},
    {"source": 3, "target": 4}, {"source": 4, "target": 5},
    {"source": 6, "target": 7}, {"source": 7, "target": 8},
    # Vertical edges
    {"source": 0, "target": 3}, {"source": 1, "target": 4}, {"source": 2, "target": 5},
    {"source": 3, "target": 6}, {"source": 4, "target": 7}, {"source": 5, "target": 8},
]

layout = GIOTTOLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    strict=True,  # Raise error if graph doesn't meet requirements
)
layout.run()

print(f"Valid input: {layout.is_valid_input}")
print(f"Total bends: {layout.total_bends}")

Use strict=False to fall back to Kandinsky-like behavior for graphs that don't meet GIOTTO's requirements:

# Graph with degree > 4 - would raise error with strict=True
layout = GIOTTOLayout(nodes=nodes, links=links, strict=False)
layout.run()  # Falls back to Kandinsky-like algorithm

Visualization

Generate visualization images for all algorithms:

uv run python scripts/visualize.py

This creates images in ./build/ showing each algorithm's output.

Algorithm Comparison

Algorithm	Best For	Complexity	Features
Random	Baselines, starting points	O(n)	Uniform distribution, reproducible
Bipartite	User-item, author-paper networks	O(n + m)	Auto-detection, crossing minimization
Cola	Constrained layouts, overlap avoidance	O(n^2) per iteration	Constraints, groups, 3D
ForceAtlas2	Large networks, community detection	O(n log n) with Barnes-Hut	Adaptive speed, degree-weighted
Fruchterman-Reingold	General graphs, aesthetics	O(n^2) per iteration	Temperature cooling
Kamada-Kawai	Small-medium graphs, stress minimization	O(n^2) per iteration	Graph-theoretic distances
Spring	Simple layouts, baselines	O(n^2) per iteration	Hooke's law
Yifan Hu	Medium-large graphs (1K-100K nodes)	O(n log n) with Barnes-Hut	Multilevel coarsening, adaptive step
Sugiyama	DAGs, hierarchies	O(n^2)	Layer-based, crossing minimization
Reingold-Tilford	Trees	O(n)	Compact, balanced
Circular	Ring structures, cycles	O(n)	Simple, predictable
Shell	Grouped/stratified data	O(n)	Degree-based grouping
Spectral	Clustering visualization	O(n^3) eigendecomp	Reveals structure
Kandinsky	UML, flowcharts, ER diagrams	O(m²)	Orthogonal edges, bend minimization, compaction, port constraints
GIOTTO	Degree-4 planar graphs	O(m²)	Bend-optimal orthogonal, validates planarity

Advanced Features

Cola: Overlap Avoidance & Constraints

from graph_layout import ColaLayoutAdapter
from graph_layout.cola.linklengths import SeparationConstraint

# Overlap avoidance
layout = ColaLayoutAdapter(
    nodes=nodes,
    links=links,
    avoid_overlaps=True,
)
layout.run()

# Hierarchical groups
groups = [{'leaves': [0, 1], 'padding': 10}, {'leaves': [2, 3], 'padding': 10}]
layout = ColaLayoutAdapter(
    nodes=nodes,
    links=links,
    groups=groups,
)
layout.run()

# Separation constraints
constraint = SeparationConstraint(axis='x', left=0, right=1, gap=50)
layout = ColaLayoutAdapter(
    nodes=nodes,
    links=links,
    constraints=[constraint],
)
layout.run()

Event System (Animation)

from graph_layout import FruchtermanReingoldLayout
from graph_layout.types import EventType

def on_tick(event):
    print(f"Alpha: {event['alpha']:.3f}")

layout = FruchtermanReingoldLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    on_tick=on_tick,
)
layout.run()

# Or register events after construction
layout = FruchtermanReingoldLayout(nodes=nodes, links=links)
layout.on(EventType.tick, on_tick)
layout.run()

3D Layout

from graph_layout.cola import Layout3D, Node3D, Link3D

nodes = [Node3D(0, 0, 0), Node3D(1, 0, 0), Node3D(0, 1, 0)]
links = [Link3D(0, 1), Link3D(1, 2), Link3D(2, 0)]

layout = Layout3D(nodes, links, ideal_link_length=1.0)
layout.start(iterations=100)

Export Formats

All layout classes support exporting to SVG, DOT (Graphviz), and GraphML formats via methods:

from graph_layout import CircularLayout

# Create and run a layout
layout = CircularLayout(
    nodes=[{"index": i} for i in range(5)],
    links=[{"source": i, "target": (i + 1) % 5} for i in range(5)],
    size=(400, 400),
).run()

# Export to SVG (web/print)
svg = layout.to_svg(node_color="#4a90d9", show_labels=True)
with open("graph.svg", "w") as f:
    f.write(svg)

# Export to DOT (Graphviz)
dot = layout.to_dot(directed=False, include_positions=True)
with open("graph.dot", "w") as f:
    f.write(dot)

# Export to GraphML (interchange format)
graphml = layout.to_graphml(include_positions=True)
with open("graph.graphml", "w") as f:
    f.write(graphml)

Orthogonal layouts (KandinskyLayout, GIOTTOLayout) automatically use orthogonal-specific export with rectangular nodes and bend points:

from graph_layout import KandinskyLayout

layout = KandinskyLayout(nodes=nodes, links=links, size=(800, 600)).run()

# SVG with orthogonal edges (polylines with bends)
svg = layout.to_svg()  # Automatically uses orthogonal rendering

# GraphML with bend point data
graphml = layout.to_graphml()  # Includes bend coordinates and port sides

# DOT with splines=ortho
dot = layout.to_dot()  # Uses box nodes and ortho splines

Standalone functions are also available:

from graph_layout import to_svg, to_dot, to_graphml

svg = to_svg(layout, node_color="#ff0000")
dot = to_dot(layout, directed=True)
graphml = to_graphml(layout)

Configuration via Properties

All layout algorithms support configuration via constructor parameters and properties:

from graph_layout import FruchtermanReingoldLayout

# Configure via constructor
layout = FruchtermanReingoldLayout(
    nodes=nodes,
    links=links,
    size=(800, 600),
    iterations=300,
    temperature=100.0,
    cooling_factor=0.95,
)

# Or modify properties after construction
layout = FruchtermanReingoldLayout()
layout.nodes = nodes
layout.links = links
layout.size = (800, 600)
layout.temperature = 50.0
layout.run()

# Access results via properties
for node in layout.nodes:
    print(f"({node.x}, {node.y})")

Module Structure

graph_layout/
    __init__.py              # Top-level exports
    base.py                  # Base classes (BaseLayout, IterativeLayout, StaticLayout)
    types.py                 # Common types (Node, Link, Group, EventType)
    basic/                   # Basic utility layouts
        random.py            # RandomLayout
    bipartite/               # Bipartite layouts
        bipartite.py         # BipartiteLayout
    cola/                    # Constraint-based layout (WebCola port)
        layout.py            # Main 2D layout
        layout3d.py          # 3D layout
        adapter.py           # ColaLayoutAdapter (Pythonic API)
        descent.py           # Gradient descent optimizer
        vpsc.py              # VPSC constraint solver
        ...
    force/                   # Force-directed layouts
        force_atlas2.py
        fruchterman_reingold.py
        kamada_kawai.py
        spring.py
        yifan_hu.py
    hierarchical/            # Tree and DAG layouts
        sugiyama.py
        reingold_tilford.py
        radial_tree.py
    circular/                # Circular layouts
        circular.py
        shell.py
    spectral/                # Spectral methods
        spectral.py
    orthogonal/              # Orthogonal layouts
        kandinsky.py         # Kandinsky layout (arbitrary degree)
        giotto.py            # GIOTTO layout (degree-4 planar, bend-optimal)
        types.py             # NodeBox, Port, OrthogonalEdge, Side
        planarization.py     # Edge crossing detection and vertex insertion
        orthogonalization.py # Bend minimization via min-cost flow
        compaction.py        # Greedy layout area minimization
        compaction_ilp.py    # ILP-based optimal area minimization
    export/                  # Export to various formats
        svg.py               # to_svg, to_svg_orthogonal
        dot.py               # to_dot, to_dot_orthogonal (Graphviz)
        graphml.py           # to_graphml, to_graphml_orthogonal

Performance

Cython Speedups

This project includes a Cython _speedups.pyx module which provides significant speedups over pure Python:

Algorithm	Cython Speedup	Notes
Fruchterman-Reingold	50-60x faster	O(n²) force calculations
ForceAtlas2	15-20x faster	Degree-weighted forces
Yifan Hu	5-7x faster	Multilevel overhead in Python
Shortest paths (Dijkstra)	5x faster	Priority queue operations

Benchmark Results

Benchmarks on random scale-free graphs (Barabási-Albert model), 50 iterations:

Algorithm	500 nodes	1,000 nodes	5,000 nodes
Random	0.001s	0.002s	0.015s
Circular	0.001s	0.002s	0.015s
Yifan Hu	0.007s	0.014s	0.077s
ForceAtlas2	0.031s	0.066s	0.402s
FR + Barnes-Hut	0.082s	0.188s	1.277s
Spectral	0.036s	0.102s	6.428s
Fruchterman-Reingold	0.059s	--	--
Kamada-Kawai	5.5s	--	--
Kandinsky	0.78s	3.6s	--

Note: FR and KK use O(n²) and are too slow for graphs >500 nodes without Barnes-Hut. Kandinsky uses O(m²) for edge crossing detection.

Algorithmic Optimizations

Beyond Cython speedups, several algorithms use asymptotically better approaches:

Function	Naive	Optimized	Technique
count_crossings()	O(m²)	O(m log m)	Merge sort inversion counting
Force repulsion	O(n²)	O(n log n)	Barnes-Hut quadtree
Yifan Hu layout	O(n²)	O(n log n)	Multilevel coarsening + Barnes-Hut

Recommendations by graph size:

• < 500 nodes: Any algorithm works well
• 500-2,000 nodes: Use Yifan Hu, ForceAtlas2, or FR+Barnes-Hut
• > 2,000 nodes: Use Yifan Hu (fastest) or ForceAtlas2 (best for communities)

Barnes-Hut Approximation

ForceAtlas2 and Yifan Hu use Barnes-Hut O(n log n) approximation by default for graphs >50 nodes. For Fruchterman-Reingold, enable it manually:

layout = FruchtermanReingoldLayout(
    nodes=nodes,
    links=links,
    use_barnes_hut=True,
    barnes_hut_theta=0.5,  # 0=exact, higher=faster but less accurate
)

Running Benchmarks

# Generate benchmark graphs
uv run python scripts/generate_benchmark_graphs.py

# Run benchmarks
uv run python scripts/benchmark_layouts.py --graphs "large_*"

Development

make test          # Run tests
make typecheck     # Type checking
make lint          # Lint code
make qa            # Run all qualtiy checks

Credits

• Cola: Port of WebCola by Tim Dwyer (see also libcola-releated papers in the adaptagrams project.
• ForceAtlas2: Based on "ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization" by Jacomy et al. (2014)
• Fruchterman-Reingold: Based on "Graph Drawing by Force-directed Placement" (1991)
• Kamada-Kawai: Based on "An Algorithm for Drawing General Undirected Graphs" (1989)
• Yifan Hu: Based on "Efficient and High Quality Force-Directed Graph Drawing" (2005)
• Sugiyama: Based on "Methods for Visual Understanding of Hierarchical System Structures" (1981)
• Reingold-Tilford: Based on "Tidier Drawings of Trees" (1981)
• Kandinsky: Based on the Kandinsky model and Tamassia's bend minimization algorithm (1987)
• GIOTTO: Based on Tamassia's "On Embedding a Graph in the Grid with the Minimum Number of Bends" (1987)

License

MIT