Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Graphs - Chris Dixon & Matt Gattis


Published on

Published in: Technology

Graphs - Chris Dixon & Matt Gattis

  1. 1. Hashtag: #hunchgraphs
  2. 2. Malcolm Gladwell Title: What Chipotle, Glenn Beck andAlien Abductions Teach Us About the Future of the Web
  3. 3. Graphs 101 Node A B Node EdgeSocial networks (Facebook): nodes are people, edges “friendship”Communication graph (Skype): nodes are people, edges communicationsSearch ranking graph (Google): nodes are pages, edges linksTaste graph (Hunch): nodes are people, edges taste similarityInterest graph (Twitter, Instagram): nodes are people, edges interest
  4. 4. First Graph Theory: Euler’s 7 bridges of Koeningsberg•Is it possible to traverse the town & cross •Convert land to nodes & bridges to edgeseach bridge exactly once? •Any node that is passed through must have even number of edges •Thus only solvable if you have 0 or 2 nodes with odd number of edges
  5. 5. Undirected Graph: Relationship Symmetric (Friendship)
  6. 6. Directed Graph: Relationship Non-symmetric (Like, follow, subscribe)One could argue that Twitter’s main innovation was making edges non-symmetric (directed), turned social network into publishing platformFacebook began as undirected friend graph but has since bolted directed“like” graph on top of it.
  7. 7. Interlude: data fun
  8. 8. Averages Twitter:Number of followers: 62.97 per userNumber of followees: 43.52 per userFacebook:Number of facebook likes: 217.2 per item (liked)Number of facebook likes: 29.30 per userBut distributions are interestingly different...
  9. 9. Twitter distributions are power curvesDistribution of # of followers you have Distribution of # of people you followSpike of “# following” curve around 20 due to old onboarding process (?)
  10. 10. Facebook friends is more like a bell curvey = number of people; x = number of friends for those people
  11. 11. Facebook “likes” similar to Twitter (since also non-symmetric?)
  12. 12. Some real world applications
  13. 13. Marketing C similar demographics to A A B purchased product communicates with A B more likely to buy than C Telecom company tested using phone call graph to use for direct mail* Targeting network neighbors of purchasers dominated other targeting techniques. Today, Facebook and many ad networks use similar targeting for online ads.* “Network-Based Marketing: IdentifyingLikely Adopters via Consumer Networks - Shawndra Hill, FosterProvost and Chris Volinsky
  14. 14. DefenseYou can infer organizational hierarchies from communication patterns.Governments use this to map rogue organizations. calls A B responds immediately calls B A responds slowly THEREFORE A B Boss Henchman
  15. 15. Google founders’ $200B ideaWords and documents are nodes, connected by occurrencePageRank: Links are directed graph Node Node
  16. 16. Gratuitous XKCD comic
  17. 17. Building graphs
  18. 18. Start with smaller graph: Bowling Pin Strategy Everyone Everyone More colleges More colleges area colleges area colleges Boston Boston Harvard• Utility is proportional to square of network coverage, but how to start?• Shrink size of the initial network and grow from there• Also try to choose a sub-network with natural ‘spillover’ effects •In this example, students at one college tend to have friends at others
  19. 19. Find clusters within existing graphsA lot of people in the 90s thought dating would be “winnertake all” - but didn’t account for clustered graph structure
  20. 20. Introducing Overlap of Buyers/Sellers can add Differentiation even in Entrenched GraphsHeterogeneous Homogenousbuyers/sellers Hybrid buyers/sellers For heterogenous buyers/sellers consider “Ladies night strategy”
  21. 21. Graph wars
  22. 22. Facebook vs Google on opening social graphsGoogle:
  23. 23. When to Interoperate?Metcalfe’s Law Corollary:Network value ~ (nodes)2 Little guy benefits more than big guy Little guy Big guy Little guy joins network and: •Big guy gains small incremental increase in connections •Little guy gains value of the many existing connections •That’s why AIM (as incumbent big player) resisted when Yahoo! & Google wanted to interoperate for IM
  24. 24. On the other hand…• Each little guy benefits more than the big guy from interoperating• But thousands of little guys relying on the big guy solidifies big guy position• Facebook realized this and introduced Facebook Apps, Connect and other“interoperating” features to prevent the “social network decay” that destroyedprevious social networks. Facebook dev platform
  25. 25. Shameless self-promotion: taste graphs
  26. 26. Tastemates as Basis of a GraphSomeone out there must enjoy the same tile/strategy games I do…And chances are they are not (yet, anyway) my friend ?Enigmo Modern Conflict Carcasonne
  27. 27. The “Cold Start” Challenge for Taste-Based PredictionsHow to provide initial recommendations for a new user? Force train, then predict Assume tastes are driven by social graph Leverage cross-vertical knowledge and adjacent known nodes in Taste Graph
  28. 28. One Cold Start Solution:Propagate Known Data to Unknown Nodes • Iteratively propogate with adjacent data • Dynamically adjust with ‘hard’ data • Lather, rinse, repeat = Known data = Unknown data
  29. 29. Applications“netflix predictionsfor everything”e-commerceand mobileFun with APIsYouzakk, AutomaticDJ
  30. 30. Since we’re at Google, some more stuff about Google
  31. 31. Communications Graphs:How Related are they to Social or Taste Graphs? My iPhone contacts include some of my friends… …but also my plumber, doctor, network administrator, United Airlines and the Chinese restaurant around the corner A lot of people were surprised that their email contacts were assumed to be active social contacts
  32. 32. Could We Use Ad Preferences to Cold Start Restaurant Recs? hotpot + 32
  33. 33. We know this person likes Classical Music, Yoga, Poetry, and Hiking 33
  34. 34. Hunch would recommend Seafood, Mediterranean, Greek, and Sushi Restaurants
  35. 35. Cross domain data can solve the “Napoleon Dynamite” problem