Charts & Visualizations
Network Graph
A visualization of interconnected entities using nodes and edges.
Definition
Network graphs (also known as node-link diagrams) represent relationships between entities by displaying them as nodes (vertices) connected by edges (links). They are particularly effective at showing complex relationships, dependencies, and flow within systems.
Examples
Simple social network connections with edge weights
Chart Visualization
This example includes an interactive chart visualization with 0 data points.
Chart type: network
Usage
Best Used For
- Visualizing relationships and connections between entities
- Analyzing social networks, organizational structures, or system dependencies
- Identifying clusters, central nodes, and bridges
- Mapping information flow or pathways
Data Requirements
[Object]
Limitations
Important Considerations
- ⚠Can become cluttered and unreadable with many nodes/edges ('hairball' effect)
- ⚠Node placement (layout algorithm) significantly impacts readability and interpretation
- ⚠Edge crossings can obscure relationships
- ⚠Difficult to represent quantitative edge weights clearly without visual overload
Best Used For
- Visualizing relationships and connections between entities
- Analyzing social networks, organizational structures, or system dependencies
- Identifying clusters, central nodes, and bridges
- Mapping information flow or pathways
Frequently asked questions
Common questions about Network Graph, answered.
What is a network graph?
A network graph — also called a node-link diagram — represents entities as nodes (dots) and the relationships between them as edges (lines). It's built to show connection structure: who links to whom, what relates to what. Node size or color can encode importance, and edge thickness can encode relationship strength. It's the standard way to visualize social networks, dependencies, co-occurrence, and any relational data.
When should I use a network graph?
Use one when the relationships between items are the point — finding clusters, central hubs, bridges, or isolated nodes — rather than the values of individual items. Good cases include audience or follower networks, content co-engagement, attribution path overlaps, and system dependencies. Avoid it when you only need to compare quantities (use a bar chart) or when there are so many nodes and edges that the result is an unreadable hairball.
How do I keep a network graph from becoming a hairball?
Several tactics: filter to the most important nodes and edges (e.g. drop weak connections below a threshold), use a force-directed layout that pulls clusters apart, color nodes by community/cluster so structure is visible, and let users zoom and filter interactively rather than showing everything at once. For very large networks, aggregate nodes into clusters or switch to a matrix view (an adjacency heat map), which scales better than node-link layouts.
What do node size and edge thickness usually mean?
They're how you encode extra dimensions. Node size commonly maps to an importance metric like degree (number of connections), centrality, or a business value such as revenue. Edge thickness maps to the strength or frequency of the relationship — number of interactions, weight of a connection. Color often encodes a category or detected community. Always document the encodings in a legend, since they're not self-evident.
Network graph vs chord diagram — what's the difference?
A network graph uses free node-link layout, so it's flexible and good for exploring overall structure and clusters among many entities. A chord diagram arranges all entities around a circle and draws connections across the middle, which is tidier for showing flows between a fixed, smaller set of categories. Use a network graph to explore structure; use a chord diagram to show relationships among a defined set of categories compactly.
