#smxlondon Everything You Need to Know About How GraphSearch Works in 15-ish Minutes


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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.

#smxlondon Everything You Need to Know About How GraphSearch Works in 15-ish Minutes

  1. 1. Everything You Needto Know About HowGraphSearch Worksin 15-ish MinutesKelvin Newman@kelvinnewman
  2. 2. Or if this presentationhas a sub-title...
  3. 3. Edges, Nodes and aFrickin’ Unicornhttp://www.escapefromcubiclenation.com/
  4. 4. Strategy DirectorSiteVisibilityA digital agency specialising in retail,travel and financial services
  5. 5. OrganiserBrightonSEO/ContentMarketing ShowTwo Free (and awesome) Conferences
  6. 6. Co-FounderClockwork TalentDecent Digital Recruitment
  7. 7. Not here to convinceyou GraphSearch willcatch on but...
  8. 8. If the area of this sliderepresents all thetraffic on the internet
  9. 9. This muchis Facebookhttp://mashable.com/2010/11/19/facebook-traffic-stats/
  10. 10. And every thingin grey is the restof the internet
  11. 11. Google, YouTube,Wikipedia, TheDaily Mail, etc.
  12. 12. your website, mywebsite, herwebsite etc.
  13. 13. we’re fightingover the scraps
  14. 14. If anyone canbuild a Google-Killerit’s Facebook...
  15. 15. There’s a fundamentaldifference betweenFacebook & Google
  16. 16. is about...
  17. 17. documents and linksJD Hancock
  18. 18. is about...
  19. 19. things and relationshipsJD Hancock
  20. 20. this difference issubtle buthuge
  21. 21. but I think it worksbetter for the webas we know itJD Hancock
  22. 22. Facebook’s datahas a far moreexplicit structurethan traditionalweb textD Hancock
  23. 23. it’s not that trickyfor Google to parse“I Like Nerf Guns”porkist
  24. 24. they could even have a go at “I was atCattlegrid in Leeds forLunch Yesterday”**if you mark it up in the right wayR_Savvy
  25. 25. but has a much harder jobunderstanding “Kelvin ismarried to Carolyn”
  26. 26. Facebook knows thathappened in 2007
  27. 27. And who attendedthe ceremony
  28. 28. And when we gotengaged
  29. 29. etc.
  30. 30. On GraphSearch you’renot really making asearch.You’re just filtering astructured database of allthe data Facebook has.
  31. 31. The Problem
  32. 32. But it’s a bloodybig databaseJD Hancock
  33. 33. 1 Billion UsersEvery Month
  34. 34. 240 Million Photo’sPer Day
  35. 35. 2.7 Billion LikesEveryday
  36. 36. People share billions ofpieces of contenteveryday
  37. 37. One trillion connectionsof a thousand differenttypes
  38. 38. 1,000,000,000,000
  39. 39. The Solution?
  40. 40. The AforementionedFrickin’ Unicorn
  41. 41. But before we get into theunicorn,let’s take a step back anddefine some terms
  42. 42. Edges & Nodes
  43. 43. Nodes are Nouns
  44. 44. Edges are Verbs
  45. 45. Every User, Page, Photo,Post & Place is a NodeJD Hancock
  46. 46. Every friendship, checkin,tag or like is an EdgeJD Hancock
  47. 47. Each Node has Meta-Datalike description, this howthe old FB Search“worked”
  48. 48. GraphSearch Allows yousearch the Edges as well as theNodesJD Hancock
  49. 49. Back the the Unicorn
  50. 50. Unicorn is and“inverted index system”
  51. 51. an inverted index (alsoreferred to as postings fileor inverted file) is an indexdata structure storing amapping from content, suchas words or numbers, to itslocations in a database file,or in a document or a set ofdocuments. The purpose ofan inverted index is to allowfast full text searches, at acost of increasedprocessing when adocument is added to thedatabase.
  52. 52. The main components ofUnicorn are:■ The index -- a many-to-many mapping from attributes(strings) to entities (fbids)■ A framework to build the index from other persistentdata and incremental updates■ A framework to retrieve entities from the index based onvarious constraints on attributes
  53. 53. Suppose your friend has fbid 1234 and lives in NewYorkand likes Downton Abbey.The index corresponding toyour friend will include the mappings:             friend:10003 → 1234            lives-in:111 → 1234            like:222 → 1234Here, we assume your fbid is 10003, and the fbid’s of NewYork and Downtown Abbey are 111 and 222 respectively.In addition, friend:10003, lives-in:111, and like:222 maymap to other users that share these attributes.
  54. 54. Unicorn makes it easy to find nodes that areconnected to another node by searching for anedge-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
  55. 55. ‘Facebook use query-independent signals to come upwith a numeric value for importance.This value is called the “static rank” of the entity.’JD Hancock
  56. 56. What makes up static rank is still upfor debate, but sensibly could beinformed by the elements ofEdgerankakathe newsfeed algo
  57. 57. Affinity
  58. 58. Weight
  59. 59. Decay
  60. 60. But what do I do?
  61. 61. The value oflegitimate likes fromwell connectedpeople just increased
  62. 62. Mark Up using theOpen GraphProtocolhttp://ogp.me/
  63. 63. You need an‘AffinityAcquisitionApproach’
  64. 64. Constantly BuildAffinitysubodh_chettri
  65. 65. Ask Questionsfontplaydotcom
  66. 66. Have a voteChodHound
  67. 67. BaitTextderekGavey
  68. 68. Tease
  69. 69. Get people totag you
  70. 70. Do good SocialMarketing
  71. 71. tl;drGraph Search is prettyawesome but works completelydifferently to Google rankingsrely exclusively on theconnections between the userand the entity ranking, so youneed do ‘good’ Facebookmarketing with a real focus onbuilding affinity.