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The Ubiquitous Graph: Two Use Cases from the Real World

The Ubiquitous Graph: Two Use Cases from the Real World

Data Science London Meetup

Tareq Abedrabbo

December 19, 2013

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  1. The Ubiquitous Graph Two Use Cases from the Real World

    Tareq Abedrabbo - Data Science London December 2013
  2. About me • CTO at OpenCredo • Working with Neo4j

    for (almost) 3 years on a number of different projects • Co-author of Neo4j in Action (Manning)
  3. “If I'm to believe Twitter, half of the earth's population

    are importing Wikipedia into Neo4j, for very obscure reasons.”
  4. • Well-defined data model • Data changes through user interactions

    • Flexible but predictable data structure(s) • Recommendation engines, social networks, etc… • Top-down design
  5. • Complex connected data that typically models real world networks

    • Integrated from a variety of different sources • Data can be unpredictable • Telco networks, utility networks, etc… • bottom-up design
  6. • Search and pattern-matching • Find a recommendation based on

    behaviour • Graph algorithms • Shortest path, disconnected components • Optimisation • Maximise oil flow while minimising water
  7. • Other requirements • Multiple starting points • Impact on

    quality of service • Abstraction of repeatable patterns
  8. 1. Start from an initial population of candidate solutions 2.

    Assess each solution using a fitness function 3. Apply genetic operators to derive a new and potentially fitter generation 4. Rinse and repeat!
  9. • Start from an initial population of candidate solutions (individuals

    or phenotypes), ideally random and large • Attribute a score to each solution using a fitness function • The only place with specific business knowledge • Apply genetic operators to create a new generation • Cross-breeding to retain best characteristics from each parent • Mutation to maintain diversity and to avoid converging to a local optima too quickly
  10. • There are other genetic operators • Copy n fittest

    solutions unchanged • Carry over n unfit candidates • Carry over n randomly chosen candidates
  11. • Pros! • All domain knowledge is encapsulated in one

    place • Generate interesting solutions including counterintuitive ones • Stop when you want! • Cons! • Fitness function can become really complex • Generated solutions are not guaranteed to be practical or pretty
  12. • Know your domain • Test non-functional aspects • Write

    code that can handle semi-structured data