Presentation at WebExpo Prague 2013

1. GraphAware
TM
by Michal Bachman
and a bit of Graph Theory
(Big) Data Science

2. GraphAware
TM
“the sexiest job in the 21st century”
HARVARD BUSINESS REVIEW
Data Science

3. GraphAware
TM
by 2018 the United States could be short up to 190,000
people with the analytical skills ... to make wise use of virtual
mountain ranges of data for critical decisions in business,
energy, intelligence, health care, finance, and other fields.
McKinsey Global Institute (2011)
Data Science

4. GraphAware
TM

5. GraphAware
TM
“hybrid computer scientist/software engineer/statistician”
The Times
Data Scientist

8. GraphAware
TM
a collection of data sets that are large and complex.
Big Data

9. GraphAware
TM
is a function of size, connectedness, and uniformity.
Data Complexity

10. GraphAware
TM
a pattern of interconnections among a set of things.
Network

11. GraphAware
TM
Social ties
Information we consume
Technological and economic systems
...
Networks

12. GraphAware
TM
a pattern of interconnections among a set of things.
Network

14. GraphAware
TM
implicit consequences of
one’s actions for the
outcomes of everyone in
the system
who is linked to whom
Structure Behaviour

15. GraphAware
TM
is the study of network structure.
Graph Theory

16. GraphAware
TM
0
25.0
50.0
75.0
100.0
2007 2008 2009 2010

17. GraphAware
TM
Leonhard Euler

18. GraphAware
TM
Seven Bridges of Königsberg

19. A
B
C D
GraphAware
TM
Graph Theory

20. A
B
C D
GraphAware
TM
Graph Theory

21. A
B
C D
GraphAware
TM
Graph Theory

22. A
B
C D
GraphAware
TM
Connected Graph

23. A
B
C D
E
F
GraphAware
TM
Connected Components

24. GraphAware
TM
is the social network of the entire world connected?
Question:

25. GraphAware
TM
(probably :-))
No.

26. GraphAware
TM
Giant Components

27. GraphAware
TM
how many giant components are there in a large, complex
network?
Question:

28. GraphAware
TM
why?
1

29. GraphAware
TM
“I read somewhere that everybody on this planet is separated
only by six other people. Six degrees of separation. Between us
and everyone else on this planet.”
Six Degrees of Separation: A Play. (John Guare)
Six Degrees of Separation

33. GraphAware
TM
average Bacon number for all
performers in the IMDb.
2.9

34. GraphAware
TM
Collaboration networks
Who-talks-to-whom graphs
Information linkage graphs
Technological networks
Natural world networks
Transport networks
...
Graphs Are Everywhere

35. GraphAware
TM
Domain interest
Proxy for a related network
Look for domain-agnostic properties
Motivations for Study

36. GraphAware
TM
People learned about new jobs through
acquaintances rather than close friends.
Granovetter’s Experiment

37. A
B C
GraphAware
TM
Triadic Closure

38. A
B C
GraphAware
TM
Triadic Closure
A
B C

39. GraphAware
TM
If two people in a social network have a friend in
common, then there is an increased likelihood
that they will become friends themselves at
some point in the future.
Triadic Closure

40. A
D
C
E
B
GraphAware
TM
Bridge

41. A
D
C
E
B
A
D
C
E
B
F H
J KG
GraphAware
TM
Local Bridge

42. A
B C
A
B C
GraphAware
TM
Strong Triadic Closure

43. A
D
C
E
BA
D
C
E
B
F H
J KG
A
D
C
E
B
F H
J KG
GraphAware
TM
Local Bridge = Weak Tie

44. A
B C
GraphAware
TM
Structural Balance

45. A
B C
GraphAware
TM
Structural Balance
A
B C
A
B C

46. A
B C
GraphAware
TM
Structural Balance
A
B C

47. A
B C
GraphAware
TM
Structural Balance
A
B C
A
B C
A
B C

48. A
B C
GraphAware
TM
Structural Balance
A
B C

49. B
C D
A
B
C D
A
GraphAware
TM
Structural Balance

50. GraphAware
TM
If a labelled complete graph is balanced, then
either all pairs of nodes are friends, or else the
nodes can be divided into two groups, X and Y,
such that each pair of people in X likes each
other, each pair of people in Y likes each other,
and everyone in X is the enemy of everyone in Y.
The Balance Theorem

51. B
C D
A
B
C
D
A
GraphAware
TM
The Balance Theorem

53. GraphAware
TM
Graph Partitioning

54. GraphAware
TM
is an open-source, fully transactional graph
database. It manipulates data in the form of a
directed property graph with labelled vertices
and edges.
Neo4j

55. name: "Drama"
type: "genre"
name: "Triller"
type: "genre"
name: "Pulp Fiction"
year: 1994
type: "movie"
DIRECTED
IS_OF_GENRE
name: "Quentin Tarantino"
type: "person"
name: "Director"
type: "occupation"
name: "Actor"
type: "occupation"
IS_OF_GENRE
ACTED_IN
name: "Samuel L. Jackson"
type: "person"
IS_A
IS_A
IS_A
ACTED_IN
role: "Jules Winnfield"
role: "Jimmie Dimmick"
GraphAware
TM
Neo4j

56. GraphAware
TM
MATCH (a)-[:ACTED_IN]->(m)
Cypher Query Language

57. GraphAware
TM
MATCH (a)-[:ACTED_IN]->(m)
Cypher Query Language

58. GraphAware
TM
START a=node(*)
MATCH (a)-[:ACTED_IN]->(m)
Cypher Query Language

59. GraphAware
TM
START a=node(*)
MATCH (a)-[:ACTED_IN]->(m)
RETURN a.name, count(m)
Cypher Query Language

60. GraphAware
TM
START a=node(*)
MATCH (a)-[:ACTED_IN]->(m)
RETURN a.name, count(m)
ORDER BY count(m) DESC
Cypher Query Language

61. GraphAware
TM
START a=node(*)
MATCH (a)-[:ACTED_IN]->(m)
RETURN a.name, count(m)
ORDER BY count(m) DESC
LIMIT 5;
Cypher Query Language

62. GraphAware
TM
==> +-----------------------------+
==> | a.name | count(m) |
==> +-----------------------------+
==> | "Tom Hanks" | 12 |
==> | "Keanu Reeves" | 7 |
==> | "Hugo Weaving" | 5 |
==> | "Meg Ryan" | 5 |
==> | "Jack Nicholson" | 5 |
==> +-----------------------------+
==> 5 rows
==>
==> 47 ms
Cypher Query Language

63. GraphAware
TM
www.graphaware.com
@graph_aware
Thank You