Explore how entities relate, reveal communities, and measure centrality—without uploading private data.
Loading…Local draft • not yet saved
01 · DATASET
02 · FILTERS
NEIGHBORHOOD EXPLORER
Explore an induced neighborhood inside the current filters. Direction is ignored for neighborhood discovery.
All nodes visible.
03 · VIEW
Community labels are deterministic estimates. Filtering changes the analytics shown on the right.
Graph workspaceSelect a node to inspect it
◎No entities in this view
Add a node or reset your filters.
Drag nodes to arrange · Drag canvas to pan · Scroll to zoom
100%
Network snapshot Visible graph
Entities0
Links0
Communities0
Components0
Graph density0%
Average degree0
Avg. clustering coefficient0
Largest component0
Category distribution
Interpretation. PageRank is calculated with a weighted random-walk model. Detected communities use weighted label propagation; they are not guaranteed to be optimal or stable across edited graphs.
Shortest route Hop count
Select two entities to inspect connectivity.
Select a node or link from the network to inspect and edit its properties.
Most connected
Highest PageRank
PageRank and degrees are computed on the filtered graph. Node size represents visible degree.
Structural analysis On demand
Exact unweighted betweenness, normalized harmonic closeness, and weak bridges/cut vertices. This can take longer on dense graphs.
Run analysis to discover central connectors and vulnerable links.
Key connectors · Betweenness
Network reach · Harmonic closeness
EXPLORATION
Relationships, made legible
Import entities and connections, then inspect connectivity, strong links, communities, local triangles and centrality. Useful for organizational diagrams, dependency mapping, research networks and graph-learning workflows.
PRIVACY BY DESIGN
Analysis stays on your device
Your graph and imports are processed in the browser. Browser storage is best-effort; export the editable JSON file for reliable backups. This is not a production graph database and does not use a server.
Dialog
Using Network Graph Analytics Studio
Create or import a relationship graph, explore node neighborhoods and layout options, and review baseline or worker-backed graph metrics.
Workflow
Load a sample or import supported JSON/CSV data; use GraphML only within the documented import bounds.
Edit nodes and edges, choose a layout, and filter the graph or inspect a selected node neighborhood.
Review the metric definitions before comparing results, then export the graph, analysis, or a project backup.
Worked example
Create three nodes labelled A, B, and C, then add directed edges A to B and B to C. Inspect the neighborhood around B and compare a shortest-hop route with the strength-weighted route before exporting a JSON project.
Limits and data handling
Metrics describe the graph data you supplied; they do not establish identity, causation, or fraud. The graph is limited to 400 nodes and 1,600 edges. Bridge analysis uses an undirected projection. Inverse-strength routes minimize the sum of 1/strength, not physical distance or maximum flow. Advanced analysis uses a Worker and browser storage; real-origin Worker and storage behavior are unverified. Keep a downloaded JSON backup for recovery.
Frequently asked questions
Does the shortest route use real-world distance?
No. Shortest-hop routes count graph edges. The strength-weighted route uses the documented inverse-strength cost, which is not geographic distance or maximum flow.
Do graph metrics prove that one person or event caused another?
No. They summarize the structure and values in the supplied graph and should not be treated as evidence of identity, intent, or causation.