DeltaGraph vs. Competitors: Which Graph Tool Wins in 2026?

DeltaGraph Tutorial: From Installation to Advanced Visuals

Introduction

DeltaGraph is a graph visualization and analysis tool designed for interactive exploration of complex networks. This tutorial walks through installation, basic usage, and advanced visualization techniques to help you build clear, informative graph visuals.

Prerequisites

• Operating system: Windows ⁄₁₁, macOS, or Linux (Ubuntu/Debian).
• Python 3.9+ (if using the Python bindings) or Node.js 16+ (for JavaScript integrations).
• Git (optional, for cloning examples).
• Basic familiarity with graph concepts (nodes, edges, attributes).

1. Installation

1.1 Using Python (recommended for data scientists)

1. Create a virtual environment:

bash
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python -m venv dg-env source dg-env/bin/activate# macOS/Linux
dg-envScriptsactivate       # Windows

2. Install DeltaGraph package (PyPI):

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pip install deltagraph 

3. Verify installation:

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python -c “import deltagraph; print(deltagraph.version)”

1.2 Using JavaScript (for web apps)

1. Initialize project and install:

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npm init -y npm install deltagraph 

2. Import in your app:

javascript
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import DeltaGraph from ‘deltagraph’;

1.3 From source

1. Clone repository:

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git clone https://github.com/deltagraph/deltagraph.git
cd deltagraph 

2. Install dependencies and build:

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pip install -r requirements.txt   # or npm install
python setup.py install           # or npm run build

2. First Graph: Loading Data and Basic Layouts

2.1 Sample dataset

Save a CSV edges file edges.csv:

csv
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source,target,weight
A,B,3
A,C,1
B,D,2
C,D,4

2.2 Load and render (Python)

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from deltagraph import Graph, Renderer 
g = Graph()
g.loadedges(‘edges.csv’)       # auto-detects CSV format
renderer = Renderer()
renderer.render(g, layout=‘force’)

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import DeltaGraph from ‘deltagraph’;

const g = new DeltaGraph.Graph();
await g.loadEdgesFromCSV(‘edges.csv’);
const renderer = new DeltaGraph.Renderer(’#canvas’);
renderer.render(g, { layout: ‘force’ });

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renderer.render(g, layout=‘hierarchical’)

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g.set_node_attribute(‘A’, ‘group’, ‘core’)
g.style_nodes(lambda n: {‘color’: ‘red’} if n.attr[‘group’]==‘core’ else {‘color’: ‘blue’})
g.styleedges(lambda e: {‘width’: e.attr.get(‘weight’,1)})

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renderer.config(labels=True, label_size=12, tooltipfields=[‘weight’])

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renderer.on(‘select’, nodes => console.log(‘selected’, nodes));

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sub = g.filternodes(lambda n: n.attr.get(‘weight’,0) > 2)
renderer.render(sub)

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g.addedge(‘E’,‘A’, weight=5)
renderer.update(g)

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renderer.registerGlyph(‘pie’, PieGlyph);
g.style_nodes(lambda n: ({ glyph: ‘pie’, glyphdata: n.attr[‘metrics’] }))

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renderer.config(edge_bundling=True, bundlingstrength=0.7)

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renderer.transitionDuration = 800;
renderer.render(g, { layout: ‘radial’ });

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renderer.enable_minimap(True)
renderer.enablefisheye(True)

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deg = g.degree_centrality()
clusters = g.louvain_communities()
path = g.shortest_path(‘A’,’D’)