About Network Graph Visualizer

Visualize network graphs with nodes and edges, community detection. Part of the DevTools Surf developer suite. Browse more tools in the Data / SQL collection.

Use Cases

• Visualize a social network graph to identify influencers and community structure.
• Map package dependencies in a codebase as a directed graph to find circular dependencies.
• Explore a knowledge graph or ontology with interactive node-link exploration.
• Visualize a citation network from research papers to identify foundational works.

Tips

• Set a minimum edge weight threshold to hide weak connections — visualizing a graph with thousands of low-weight edges makes it unreadable.
• Use community detection coloring to visually cluster related nodes before examining connections within and between communities.
• Export the graph with the node positions fixed (static layout JSON) to share a consistent view with collaborators.

Fun Facts

• The six-degrees-of-separation hypothesis, originally proposed by Frigyes Karinthy in 1929, was empirically validated by a 2011 Facebook study that found the average shortest path between any two users was 4.74 — not 6.
• Graph theory was founded by Leonhard Euler in 1736 with his solution to the Königsberg Bridge Problem — whether you could walk through the city crossing each of its 7 bridges exactly once. The answer (no) proved the concept of Eulerian paths.
• PageRank, the algorithm behind Google's original search ranking, is a network graph algorithm that assigns importance to web pages based on the number and quality of incoming links — essentially treating the web as a directed graph.

FAQ

What input formats does it accept?

JSON node-link format (nodes array + edges array with source/target), adjacency matrix CSV, GraphML XML, and Gephi GEXF files. CSV edge lists (source, target, weight) are also supported.

Which graph algorithms does it support?

PageRank, betweenness centrality, degree centrality, clustering coefficient, community detection (Louvain), shortest path (Dijkstra), and connected component analysis.

How large a graph can it render?

Up to ~5,000 nodes and 20,000 edges in the browser with force-directed layout. Larger graphs need server-side layout computation — WebGL rendering mode handles up to 50,000 nodes at reduced functionality.