intro to graphs for hr analytics

An  Intro  to  Graphs  for  

HR  Analy)cs  rik@neotechnology.com  

Agenda  •  About  Graphs  •  About  Graph  Databases  •  Why  Graph  Databases  ma=er  for  HR  Analy@cs  –  Short  demonstra@on  

•  Case  Studies  •  Q&A  

Introduc@on:  about  Graphs  

Meet ���Leonhard Euler •  Swiss  mathema@cian  •  Inventor  of  Graph  Theory  (1736)  

Königsberg  (Prussia)  -­‐  1736  

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About  Graph  Databases  

So  what  is  a  graph  database?  

•  OLTP  database  – “end-­‐user”  transac@ons  

•  Model,  store,  manage  data  as  a  graph  

What  is  a  graph?  Node  

Rela@onship  

Contrast  with  Rela@onal  

Graphs are often referred to as “Whiteboard Friendly”. The data model reflects the way a domain expert would naturally

draw their data on a whiteboard “The schema is the data”. Schema flexibility allows the system

to change in response to a changing environment

What  are  graphs  good  for?  

Complex  Querying  

Examples  of  complex  queries?  1.  Semi-­‐structure  in  datasets  

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– Normaliza@on  introduces  complexity  

– Forces  developers  to  develop  all  kinds  of  logic  to  deal  with  this  variability  in  their  applica@on  logic  

Examples  of  complex  queries:  2.  Connectedness  in  data  

Lots  of  normalized  rela@onships  between  the  different  en@@es,  forces  developers  to  do  •  Deep  joins  •  Recursive  joins  •  Pathfinding  opera@ons  •  “open-­‐ended”  queries  

Examples  of  Connectedness  

Graph  Querying  

Querying  a  Graph  

•  “Graph  local”  vs  “Graph  global”  – Contextualized  “ego-­‐centric”  queries  

•  “Parachute”  into  graph  – Start  node(s)  

•  Found  through  Index  lookups  

•  Crawl  the  surrounding  graph  – 2  million+  joins  per  second  

•  No  more  Index  lookups:    Index-­‐free  adjacency  

Queries:  Pa=ern  Matching  

Pa=ern  

Short  demo:  HR  Analy@cs  

Domains  that  jump  out  

•  The  REAL  Enterprise  Social  Network  –  Be=er  understanding  of  the  “coffee-­‐room”  network  

•  Recruitment  – Micro-­‐targe@ng  –  Social  integra@on  

•  Competency  management  –  Smart  matching  –  Taxonomies  –  Op@miza@on  algorithms  

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It  always  starts  with  a  MODEL  

Then  for  some  Queries  

•  Network  Analy@cs  – Degree  Centrality  – Betweenness  Centrality  – PageRank  

•  Recommenda@ons  – Triadic  closures  – Complex  pa=ern  matching  

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Use  Cases  (neo4j.com/use-­‐cases)  

Customers  (neo4j.com/customers)  

Graph  Gists  (h=p://gist.neo4j.org/)  

Neo Technology, Inc Confidential

Neo4j License Overview

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Future  trainings  &  events!  

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Neo  Technology  www.neotechnology.com    Neo4j  www.neo4j.org      rik@neotechnology.com  or  +32  478  686800  

Q&A,  Conclusion,  Next  Steps