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Hashtag: #hunchgraphs
Malcolm Gladwell Title:What Chipotle, Glenn Beck and
Alien Abductions Teach Us About the Future of the Web
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
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?
Undirected Graph: Relationship Symmetric (Friendship)
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.
Interlude: data fun
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...
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
Facebook friends is more like a bell curve
y = number of people; x = number of friends for those people
Facebook “likes” similar to Twitter (sincealso non-symmetric?)
Some real world applications
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
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
Google founders’ $200B idea
Words and documents are nodes, connected by occurrencePageRank: Links are directed graph
Node Node
Gratuitous XKCD comic
Building graphs
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
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
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”
Graph wars
Facebook vs Google on opening social graphs
Google:
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
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
Shameless self-promotion: taste graphs
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
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
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
Applications
Fun with APIs
“netflix predictionsfor everything”
e-commerceand mobile
Youzakk, AutomaticDJ
Since we’re at Google, some more stuff about Google
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
Could We Use Ad Preferences to Cold Start Restaurant Recs?
32
hotpot+
We know this person likes Classical Music, Yoga, Poetry, and Hiking
33
Hunch would recommend Seafood, Mediterranean, Greek, and Sushi Restaurants
Cross domain data can solve the “Napoleon Dynamite” problem