| name | tigergraph |
| description | Distributed graph analytics platform with GSQL query language. Use when running fraud detection on transaction networks, analyzing supply chain dependencies, building customer 360 views, computing PageRank on billion-edge graphs, or querying deep multi-hop relationships in real time. |
| version | 1.0.0 |
| author | BytesAgain |
| homepage | https://bytesagain.com |
| source | https://github.com/bytesagain/ai-skills |
| tags | ["tigergraph","graph-database","gsql","analytics","fraud-detection","supply-chain"] |
TigerGraph Reference
Distributed graph analytics platform for real-time deep-link analysis on massive datasets. TigerGraph uses GSQL, a SQL-like graph query language designed for parallel traversal across 100+ billion edges with sub-second response times.
When to Use
- Fraud detection: identify suspicious transaction rings and money laundering patterns
- Supply chain: trace multi-hop dependencies and find bottleneck nodes
- Customer 360: merge entity data across multiple sources into a unified graph
- Recommendation engines: collaborative filtering at scale using graph traversal
- Network analytics: map telecom or IT infrastructure relationships
- Knowledge graphs: build and query enterprise knowledge bases
Commands
| Command | Description |
|---|
intro | Architecture overview, TigerGraph vs Neo4j comparison, use cases, installation |
gsql | GSQL schema definition, data loading jobs, queries (ad-hoc and installed), PageRank implementation |
api | REST API endpoints for vertices/edges/queries, pyTigerGraph Python SDK, GraphStudio visual IDE |
Requirements
- No external dependencies — outputs reference documentation only
- No API keys required
- No network access needed
Feedback
https://bytesagain.com/feedback/
