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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​

  • Neo4j Graph Data Science

    • Enterprise graph analytics
    • Scalable algorithms
    • Rich visualization

  • 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