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Hashtag: #hunchgraphs

Graphs - Chris Dixon & Matt Gattis

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Page 1: Graphs - Chris Dixon & Matt Gattis

Hashtag: #hunchgraphs

Page 2: Graphs - Chris Dixon & Matt Gattis

Malcolm Gladwell Title:What Chipotle, Glenn Beck and

Alien Abductions Teach Us About the Future of the Web

Page 3: Graphs - Chris Dixon & Matt Gattis

Graphs 101

A BNodeNode

Edge

Social networks (Facebook): nodes are people, edges “friendship”

Communication graph (Skype): nodes are people, edges communications

Taste graph (Hunch): nodes are people, edges taste similarity

Search ranking graph (Google): nodes are pages, edges links

Interest graph (Twitter, Instagram): nodes are people, edges interest

Page 4: Graphs - Chris Dixon & Matt Gattis

First Graph Theory: Euler’s 7 bridges of Koeningsberg

•Convert land to nodes & bridges to edges•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

•Is it possible to traverse the town & cross each bridge exactly once?

Page 5: Graphs - Chris Dixon & Matt Gattis

Undirected Graph: Relationship Symmetric (Friendship)

Page 6: Graphs - Chris Dixon & Matt Gattis

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 platform

Facebook began as undirected friend graph but has since bolted directed “like” graph on top of it.

Page 7: Graphs - Chris Dixon & Matt Gattis

Interlude: data fun

Page 8: Graphs - Chris Dixon & Matt Gattis

Averages

Twitter:

Number of followers: 62.97 per userNumber of followees: 43.52 per user

Facebook:Number of facebook likes: 217.2 per item (liked)Number of facebook likes: 29.30 per user

But distributions are interestingly different...

Page 9: Graphs - Chris Dixon & Matt Gattis

Twitter distributions are power curves

Spike of “# following” curve around 20 due to old onboarding process (?)

Distribution of # of followers you have Distribution of # of people you follow

Page 10: Graphs - Chris Dixon & Matt Gattis

Facebook friends is more like a bell curve

y = number of people; x = number of friends for those people

Page 11: Graphs - Chris Dixon & Matt Gattis

Facebook “likes” similar to Twitter (sincealso non-symmetric?)

Page 12: Graphs - Chris Dixon & Matt Gattis

Some real world applications

Page 13: Graphs - Chris Dixon & Matt Gattis

Marketing

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, Foster Provost and Chris Volinsky

A B

purchased product

C

similar demographics to A

communicates with A

B more likely to buy than C

Page 14: Graphs - Chris Dixon & Matt Gattis

DefenseYou can infer organizational hierarchies from communication patterns.

Governments use this to map rogue organizations.

Boss Henchman

A Bcalls

responds immediately

ABcalls

responds slowly

A B

THEREFORE

Page 15: Graphs - Chris Dixon & Matt Gattis

Google founders’ $200B idea

Words and documents are nodes, connected by occurrencePageRank: Links are directed graph

Node Node

Page 16: Graphs - Chris Dixon & Matt Gattis

Gratuitous XKCD comic

Page 17: Graphs - Chris Dixon & Matt Gattis

Building graphs

Page 18: Graphs - Chris Dixon & Matt Gattis

Start with smaller graph:Bowling Pin Strategy

Har

vard

Bos

ton

area

col

lege

s

Bos

ton

area

col

lege

s

Mor

e co

llege

s

Mor

e co

llege

s

Eve

ryon

e

Eve

ryon

e• 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

Page 19: Graphs - Chris Dixon & Matt Gattis

Find clusters within existing graphs

A lot of people in the 90s thought dating would be “winner take all” - but didn’t account for clustered graph structure

Page 20: Graphs - Chris Dixon & Matt Gattis

Introducing Overlap of Buyers/Sellers can add Differentiation even in Entrenched Graphs

Heterogeneous buyers/sellers Hybrid

Homogenous buyers/sellers

For heterogenous buyers/sellers consider “Ladies night strategy”

Page 21: Graphs - Chris Dixon & Matt Gattis

Graph wars

Page 22: Graphs - Chris Dixon & Matt Gattis

Facebook vs Google on opening social graphs

Google:

Page 23: Graphs - Chris Dixon & Matt Gattis

When to Interoperate?

Metcalfe’s LawNetwork value ~ (nodes)2

Corollary: Little guy benefits more than 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

Little guyBig guy

Page 24: Graphs - Chris Dixon & Matt Gattis

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 destroyed previous social networks.

Facebook dev platform

Page 25: Graphs - Chris Dixon & Matt Gattis

Shameless self-promotion: taste graphs

Page 26: Graphs - Chris Dixon & Matt Gattis

Tastemates as Basis of a Graph

CarcasonneModern Conflict

?

Enigmo

Someone out there must enjoy the same tile/strategy games I do…And chances are they are not (yet, anyway) my friend

Page 27: Graphs - Chris Dixon & Matt Gattis

The “Cold Start” Challenge for Taste-Based Predictions

How 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

Page 28: Graphs - Chris Dixon & Matt Gattis

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

Page 29: Graphs - Chris Dixon & Matt Gattis

Applications

Fun with APIs

“netflix predictionsfor everything”

e-commerceand mobile

Youzakk, AutomaticDJ

Page 30: Graphs - Chris Dixon & Matt Gattis

Since we’re at Google, some more stuff about Google

Page 31: Graphs - Chris Dixon & Matt Gattis

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

Page 32: Graphs - Chris Dixon & Matt Gattis

Could We Use Ad Preferences to Cold Start Restaurant Recs?

32

hotpot+

Page 33: Graphs - Chris Dixon & Matt Gattis

We know this person likes Classical Music, Yoga, Poetry, and Hiking

33

Page 34: Graphs - Chris Dixon & Matt Gattis

Hunch would recommend Seafood, Mediterranean, Greek, and Sushi Restaurants

Page 35: Graphs - Chris Dixon & Matt Gattis

Cross domain data can solve the “Napoleon Dynamite” problem

Page 36: Graphs - Chris Dixon & Matt Gattis