acebook’s recently unveiled Graph Search is currently being rolled out as a small private beta, but over time all billion-plus users will have access to it, so it’s something all online marketers should start thinking about. While it’s currently not a direct threat to Google, it’s very good at people search, local & vertical search and entertainment search. Come hear our panel discuss how graph search is unique, how it will change SEO, and the opportunities and challenges it’s likely to present for advertisers and marketers.

1. Everything You Need
to Know About How
GraphSearch Works
in 15-ish Minutes
Kelvin Newman
@kelvinnewman

2. Or if this presentation
has a sub-title...

3. Edges, Nodes and a
Frickin’ Unicorn
http://www.escapefromcubiclenation.com/

4. Strategy Director
SiteVisibility
A digital agency specialising in retail,
travel and financial services

5. Organiser
BrightonSEO/Content
Marketing Show
Two Free (and awesome) Conferences

6. Co-Founder
Clockwork Talent
Decent Digital Recruitment

7. Not here to convince
you GraphSearch will
catch on but...

8. If the area of this slide
represents all the
traffic on the internet

9. This much
is Facebook
http://mashable.com/2010/11/19/facebook-traffic-stats/

10. And every thing
in grey is the rest
of the internet

11. Google, YouTube,
Wikipedia, The
Daily Mail, etc.

12. your website, my
website, her
website etc.

13. we’re fighting
over the scraps

14. If anyone can
build a Google-Killer
it’s Facebook...

15. There’s a fundamental
difference between
Facebook & Google

16. is about...

17. documents and links
JD Hancock

18. is about...

19. things and relationships
JD Hancock

20. this difference is
subtle but
huge

21. but I think it works
better for the web
as we know it
JD Hancock

22. Facebook’s data
has a far more
explicit structure
than traditional
web text
D Hancock

23. it’s not that tricky
for Google to parse
“I Like Nerf Guns”
porkist

24. they could even have a go at “I was at
Cattlegrid in Leeds for
Lunch Yesterday”*
*if you mark it up in the right way
R_Savvy

25. but has a much harder job
understanding “Kelvin is
married to Carolyn”

26. Facebook knows that
happened in 2007

27. And who attended
the ceremony

28. And when we got
engaged

29. etc.

30. On GraphSearch you’re
not really making a
search.
You’re just filtering a
structured database of all
the data Facebook has.

31. The Problem

32. But it’s a bloody
big database
JD Hancock

33. 1 Billion Users
Every Month

34. 240 Million Photo’s
Per Day

35. 2.7 Billion Likes
Everyday

36. People share billions of
pieces of content
everyday

37. One trillion connections
of a thousand different
types

38. 1,000,000,000,000

39. The Solution?

40. The Aforementioned
Frickin’ Unicorn

41. But before we get into the
unicorn,
let’s take a step back and
define some terms

42. Edges & Nodes

43. Nodes are Nouns

44. Edges are Verbs

45. Every User, Page, Photo,
Post & Place is a Node
JD Hancock

46. Every friendship, checkin,
tag or like is an Edge
JD Hancock

47. Each Node has Meta-Data
like description, this how
the old FB Search
“worked”

49. GraphSearch Allows you
search the Edges as well as the
Nodes
JD Hancock

50. Back the the Unicorn

51. Unicorn is and
“inverted index system”

52. an inverted index (also
referred to as postings file
or inverted file) is an index
data structure storing a
mapping from content, such
as words or numbers, to its
locations in a database file,
or in a document or a set of
documents. The purpose of
an inverted index is to allow
fast full text searches, at a
cost of increased
processing when a
document is added to the
database.

53. The main components of
Unicorn are:
■ The index -- a many-to-many mapping from attributes
(strings) to entities (fbids)
■ A framework to build the index from other persistent
data and incremental updates
■ A framework to retrieve entities from the index based on
various constraints on attributes

54. Suppose your friend has fbid 1234 and lives in NewYork
and likes Downton Abbey.The index corresponding to
your friend will include the mappings:
friend:10003 → 1234
lives-in:111 → 1234
like:222 → 1234
Here, we assume your fbid is 10003, and the fbid’s of New
York and Downtown Abbey are 111 and 222 respectively.
In addition, friend:10003, lives-in:111, and like:222 may
map to other users that share these attributes.

55. Unicorn makes it easy to find nodes that are
connected to another node by searching for an
edge-type combined with an input node.
E.g.:
■Your friends: friend:10003
■People who live in new york: lives-in:111
■People who like downtown abbey: like:222

56. ‘Facebook use query-independent signals to come up
with a numeric value for importance.
This value is called the “static rank” of the entity.’
JD Hancock

57. What makes up static rank is still up
for debate, but sensibly could be
informed by the elements of
Edgerank
aka
the newsfeed algo

59. Affinity

60. Weight

61. Decay

62. But what do I do?

63. The value of
legitimate likes from
well connected
people just increased

64. Mark Up using the
Open Graph
Protocol
http://ogp.me/

65. You need an
‘Affinity
Acquisition
Approach’

66. Constantly Build
Affinity
subodh_chettri

67. Ask Questions
fontplaydotcom

68. Have a vote
ChodHound

69. Bait
Text
derekGavey

70. Tease

71. Get people to
tag you

72. Do good Social
Marketing

73. tl;dr
Graph Search is pretty
awesome but works completely
differently to Google rankings
rely exclusively on the
connections between the user
and the entity ranking, so you
need do ‘good’ Facebook
marketing with a real focus on
building affinity.