Graph Analytics Platforms
Platforms for analyzing and discovering patterns in graph and network data.
Supported Solution Fields
When to Use
- When analyzing complex networks
- When discovering graph patterns
- When mapping relationships
- When visualizing networks
When Not to Use
- When data isn't relationship-based
- When simple statistical analysis suffices
- When real-time processing is needed
Tradeoffs
- Scale vs Performance: Larger graphs need more resources
- Flexibility vs Complexity: More features mean steeper learning curve
- Visualization vs Size: Large graphs are harder to visualize
Commercial Implementations
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Neo4j Graph Data Science
- Enterprise graph analytics
- Scalable algorithms
- Rich visualization
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TigerGraph
- Distributed graph processing
- Pattern matching
- Real-time analytics
Common Combinations
- Social network analysis
- Fraud detection
- Knowledge graphs
Case Study: Financial Network Analysis
A bank implemented graph analytics for fraud detection:
Challenge
- Complex transaction networks
- Hidden fraud patterns
- Real-time monitoring needs
Solution
- Implemented Neo4j
- Pattern matching algorithms
- Network visualization
Results
- 40% better fraud detection
- Faster pattern identification