TalkMiner - Graphs with Chris Dixon

Title: Graphs with Chris Dixon

graphs 101 i go[e 1 node node sodas networks (facebook): nodes are people, edges friendship communication graph (skype): nodes are people. edges communications search ranking graph (google): nodes are pages. edges links taste graph (hunch): nodes are people. edges taste similarity edge interest graph (twitter, instagram): nodes are people, edges interest

graphs 101 social networks (facebook): nodes are people, edges lriendshipe communication graph (skype): nodes are people. edges communications search ranking graph (google): nodes are pages. edges links taste graph (hunch): nodes are people. edges taste similarity node edge node interest graph (twitter, instagram): nodes are people, edges interest

graphs 101 node ___________ node edge social networks (facebook): nodes are people. edges lriendshipe communication graph (skype): nodes are people, edges communications search ranking graph (google): nodes are pages, edges links taste graph (hunch): nodes are people. edges taste simharity interest graph (twitter, instagram): nodes are people, edges interest

first graph theory: euler’s 7 bridges of koeningsberg ‘is it possibe to traverse the town & cross each brkge exty once? convert ‘and to nos & bikis to edges •any node at psed through mu have ev iiimber ci edges ‘thus xdy sdvate it you have 0 2 no wth odd mirr of edges

first graph theory: euler’s 7 bridges of koeningsberg -ispossibeto baversethetown &as eh idge ectty ? cwert land to nodes & bñdges to edges •any node that is passed tirnxigfl mu have even nimiber of edges ‘thus y solvable if you have 0 or 2 no with odd num of edges

first graph theory: euler’s 7 bridges of koeningsberg convect iam to nodes & *ges to es any node th s psed thn4i must have even number edges •ttwis cdy svabse i(yj have 0 2 nodes with odd number c edges he le 7 goie 1 .45 t posseie to traverse the town &crs each bridge exactty once?

undirected graph: relationship symmetric (friendship) il • c a . — - i—--• -• — i 4 j- _• -i__r — r,-._——. - - __a . cttkc ‘— -

interlude: data fun

averages twitter number of followers: 62.97 per user number of followees: 43.52 per user facebook: number of facebook hkes: 217.2 per item (liked) number of facebook likes: 29.30 per user but distributions are interestingly differenl. —

1— — averages twitter number of followers: 62.97 per user number of followees: 43.52 per user facebook: number of facebook likes: 2172 per item (liked) number of facebook likes: 29.30 per user but distributions are interestingly different..

averages twitter number of followers: 62.97 per user number of followees: 4152 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..

averages twitter number of followers: 6197 per user number of followees: 43.52 per user facebook: number of facebook likes: 2172 per item (liked) number of facebook likes: 29.30 per user but distributions are interestingly different.. i’il

twitter distributions are power curves distribution oft of fdllowec5 you have thbn of # of people you follow

twitter distributions are power curves distribution of # of followers you have distribution of i of people you follow a—— l spike of # followinge curve around 20 due to old onboarding process (?)

twitter distributions are power curves distribution of i of followers you have i go li distribution of i of people you follow a— —p s a-. — — w 5— ws s — rn - p 8 p 0 * ? • w spike of # foiiowing’ curve around 20 due to old onboardirig process (?)

twitter distributions are power curves distribution of s of followers you distribution of # of people you follow spike of # following curve around 20 due to old onboarding process (?)

twitter distributions are power curves distribution of i at followes you have distribution of i of people you follow w • spike of # foilowing’ curve around 20 due to old onboarding process (?)

twitter distributions are power curves distribution of # of followers you have distribution of # of op1e you follow spike of # foilowing curve around 20 due to old onboarding process (?)

‘i 1 go1e facebook friends is more like a bell curve i s 9 13 17 21 x) 37 41 ii 1 57 61 73 77 si 93 97 y = number of people; x = number of friends for those people

i facebook friends is more like a bell curve 1 5 9 13 17 21 37 *1 49 57 61 73 77 si 97 y = number of people: x = number of ffiends for those people i

facebook friends is more like a bell curve i ¶ 5 9 13 it 21 fl *1 49 1 57 61 73 77 81 v y = number of people; x = number of ffiends for those people

of f&aoo’i l.s 0 facebook “likes” similar to twitter (since also non-symmetric?) , 5 , ?3 17 21 25 29 fl v 41 .l5 53 57 61 6 73 77 31 1% 33 97

f’ t v. purchased product marketing • — = tcs tii ll !go1e i similar demographics to a 4. _._e_.._.._. 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 — - * - shawnar.i 4t i foster

marketing similar demographics to a 4..... 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 arid many ad networks use similar targeting for online ads —:-- -— -“- - sh.awra4t1. fser prcs an crs .c:’s d

_pi / -4 i - ___ go gle therefore 4 henchman a defense you can infer organizational hierarchies from communication patterns. governments use this to map rogue organizations. calls responds immediately calls 4.. . . . responds slowly boss

google founders’ $200b idea wocds and documents are nodes. connected by occurrence pagerank: links are directed graph node node ;1] yjr ws eme’ — l s9;0] —---•_: . __ — a-. — a —— • zam• — l arr crs te ov) ‘_...., t lr. p.. —

google founders’ $200b idea words and documents are nodes. connected by occurrence pagerank: links are directed graph — - - node ;1] __ ___ y, en.r —;0] — — • —— — node t. :( — z t,çan ay to ci p trz -. p._ — - r. ‘ li -,

google founders’ $200b idea words and documents are nodes, connected by occurrence pagerank: links are dwected graph node node _______ ----- ____ __ __ e1li. army to ck t.r.j’_1—i. p i—i ;1] ‘yql- ne 5 ett - — ;0] — - — —- —. - — p —

google founders’ $200b idea words and documents are nodes, connected by occurrence pagerank: links are directed graph node node --“— _____ - -. —-4t to cvàh •j ‘r. —

zinggraphs

start with smaller graph: bowling pin strategy • utility is proportional to square of network coverage, but how to start? • shnnk size of me initial network arid grow from there • also try to choose a sub-network with natural ‘spillover’ effects •ln this example, students at one college tend to have friends at others i n >

start with smaller graph: bowling pin strategy • 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 •f n this example, students at one college tend to have friends at others a 0 a h. —

find clusters within existing graphs i’ll; , a lot of people in the 90s thought dating would be winner take all - but didn’t account for clustered graph structure

lf go ‘gte introducing overlap of buyers/sellers can add differentiation even in entrenched graphs heterogeneous homogenous hyt)ñd buyers/sellers openlable li*: aii _rn4s li for heterogenous buyers/sellers consider ladies night strategy

introducing overlap of buyers/sellers can add differentiation even in entrenched graphs heterogeneous buyers/sellers hybñd homogenous buyers/sellers opentable • harmony’ p 4word ) linked [ - for heterogenous buyers/sellers consider ladies night strategy”

introducing overlap of buyers/sellers can add differentiation even in entrenched graphs heterogeneous homogenous buyers/sellers hybnd opentable li aw () for heterogenous buyerslsellers consider ladies night 5frateg

introducing overlap of buyers/sellers can add differentiation even in entrenched graphs heterogeneous buyers/sellers 0 pentabie hybrid homogenous buyers/sellers i’ - — — for heterogenous buyers/sellers consider ladies night strategy” 7tioflstqr 7 f.;: i - ic v

introducing overlap of buyers/sellers can add differentiation even in entrenched graphs heterogeneous homogenous buyers/sellers hy buyers/sellers - openiable “.3 li ;1] gaycom;0] for heterogenous buyers/sellers consider ladies night strategy”

introducing overlap of buyers/sellers can add differentiation even in entrenched graphs heterogeneous homogenous buyers/sellers hybnd opintable l:’* ..___j —w il) ;1] gaym;0] for heterogenous buyerslsellers consider ‘ladies night strategy

when to interoperate? metcalfe’s law network vakie - (nodesj corollary litite guy benefits ne than big guy le jy joins rwtwork and: ‘big guy gáns smal ienntaj inaease m cnetkws ‘le guy garns vaàue & the rriy ex!sting conneis •ths wtiy aim (as irintent big pay) r?sd wnen yahc! & googe vard to int’operate for im big guy little guy

when to interoperate? metcalfe’s law netwcxk varie — (nod corollary. littse guy benefit more than big gu big guy little guy løe :j-- rita ncre.sr l:t1egy 3 •—‘ •jwd -- &gvoew3kt- cqecate’c’

when to interoperate? corotiary liwe guy benefits cre than bq guy big guy l giqs ret1, 4 rea .-‘--: -‘‘: d! & gooe warte z -‘ ercçate fr m metcalfe’s law netwark vakie - (noiies) little guy 0 v

when to intemperate? metcawe’s law network vamie - (nos corollary: little guy benefits nre thaws big guy l rvetwcrk awi .e rrai rease — - ‘:‘ ?jm - - &gooewac,c. big guy little guy

i when to interoperate? big guy guy ui’ iyrei a 6k! . rceas e j1hs uethen-3-, ‘“: •thas .‘y um cis t’’ & gooqe .yar!e2 :d -. i jii go ‘gle metcalfe’s law netwk ahie - (riod 1 corollary: uttie guy benets nm tin b guy 1

when to interoperate? metcalfe’s law netwtxk va1i - nocs)3 corollary: lime guy beneñts me thai bg guy ‘-a l* w ic retwc’- art “bir y : t3i ;rmetaj encease te guy au’.s ; e —arv -‘ •‘: ,j -. - & gooqe warte go g1e bg gur little guy -

on the other hande.. i —. • — 1 -— _____ facebook dev platform p r eacn liffle guy benefits more than the big guy from interoperating butthousands of little guys relying on the big gu lidifies big guy position • facebook reaiized this and introduced facebook apps. connect and other ainteroperatinge features to prevent the social network deca’ that destroyed previous social networks. j __ ____-z

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 intemp&ating features to prevent the ‘social network decay that destroyed previous social networks. / -v facebook dev platform /

shameless seifpromotion: taste graphs i i

tastemates as basis of a graph someone out there must enjoy the same tile/strategy games i do... and chances are they are not (yet, anyway) my friend enigmo - modem conflict i ---‘-e i __ 4 1. .9 (.arcasonne

tastemates as basis of a graph someone out there must enjoy the same tilelstrategy games i do.. and chances are they are not (yet anyway) my ffiend -j modem conflict - -ç i - w; _tl enigmo a, -4 r (arcasonne

tastemates as basis of a graph someone out there must enjoy the same tile/strategy games i do.... and chances are they are not (yet anyway) my friend l.1 i-- 1 l p. i enigmo moiem conflict carcasonn e

the “cold starr challenge for taste-based predictions p how to provide initial recommendations for a new user? go ‘g1e _____________ ii netfiik forcetrainthenpredict facebook metastesñvenbysociaigraph kleverage cross-vertical knowledge and u flc adjacent known nodes in taste graph

the “cold start challenge for taste-based predictions n f t f [i force train, then preiict facebook assume tastes are driven by social graph kleverage cross-vertical knowledge and u flc adjacent known nodes in taste graph

the “cold start” challenge for taste—based predictions how to proviae initial recommendations for a new user? netf[1 forcetrainthenpredict ;1] ___ facebook;0] hunch assume tastes are driven by social graph everage crossverticai nowieage ano adjacent known nodes in taste graph

the “cold start” challenge for taste-based predictions i-tow to provide initial recommendations for a new user? force train, then predict facebook assume tastes are driven by social graph leverage cross-vertical knowledge and adjacent known nodes in taste graph

the starts challenge for taste—based predictions how to prowle initai recommengatlons for a ew user’ force train, then predict r cebook assume es are drtven by social graph everage cross-verucai riowieage ano u fl c ajacent known nodes in taste graph

the “cold start” challenge for taste-based predictions how to provide initial recommendations for a new user? n ft f [i x force train. then predict assume tastes are driven by social graph beverage cross-verticat nowteage ana h u fl c h adjacent known nodes in taste graph

one cold start solution: propagate known data to unknown nodes • iteratively propagate with adjacent data • dynamically adjust with ‘hard’ data • lath er rinse, repeat known data 0= unknown data i 4 .

fun with apis youzakk. automaticdj 1_— — i ri benjamin gtettzman • —4 \_. i applications gog1e fbi., i. 3 — thetflix predictions for everything e-commerce and mobile [1 i— - - b

fun with apis youzakk. automaticdj applications i u i i1i go ‘gle _i4 aofletfijx predidions for everything e-cornmerce and mobile --..t-r — lp_ - l. njamin gli

applications afletfljx predictions for everything llei f f -: i 3i’*a- f — fun with apis youzakk, automaticdj liz _‘‘i_i j. e-commerce and mobile all i — mtn gan

netflix predictions for everything e-commerce and mobile applications — , - i—.—.’ — i. _! in fun with apis youzakk. automaticdj benjamin gleitzmari ‘4 i

‘netffix predictions for everything applications e-cornmerce and mobile — t.__— —-p.-.,, [.j: ti . — — ._! qi i. -‘---:=i. 1 ,- -i-il fun with apis youzakk. automaticdj ijaniin glej7il

communications graphs: how related are they to social or taste graphs? ______ my iphone contacts include some of my ñiend& __ but also my plumber, doctor, network admnistrator united airlines and the chinese restaurant around the corner — ‘ a — — — a lot of people were surprised that their email contacts were assumed to be active social contacts :- -

i i could we use ad preferences to cold start restaurant recs? got igle hotpot + google _ — — — —— — — , f. — & i . t- - ____ ____ a—s, a n a r - e -._ .-‘---. r _____ *rl — a -‘ a-...a-.---— —- ____ ____• —- a t - _____ a • - a — - i. ____ ti ____ i • - _____ ____ - i - - _____ tr ‘ é cz ____ . t ca-a. -i..- a — _____ “—f-- — — —i----

we know this person likes classical music, yoga. poetry, and hiking goügle - — — —--‘- ‘—-— ‘.- - i ai-...- rr4p i - --°‘ i j ws * i shr.. ij . • — a c. - t tz a : —--- • ----r l i 3r ,a.——--’_- - 1. — .,l a 34

cross domain data can solve the “napoleon —— —— — dynamite” problem — —..- —--— _i-i. i’•• i’ a — a a 1 - e-. -— — si go ‘gle — — _,._i_____ z a — a i — -

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Published: 2011-03-01
Channel: GoogleTechTalks
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Abstract: Google Tech Talk
February 16, 2011

Presented by Chris Dixon

ABSTRACT

It has become customary to use "graph" to refer to the underlying data structures at social networks like Facebook. (Computer scientists call the study of graphs "network theory," but on the web the word "network" is used to refer to the websites themselves). A graph consists of a set of nodes connected by edges. The original internet graph is the web itself, where webpages are nodes and links are edges. In social graphs, the nodes are people and the edges friendship. Edges are what mathematicians call relations.

Graphs can be implicitly or explicitly created by users. Facebook and Twitter's graphs were explicitly created by users. At Hunch we've created what we call the taste graph. We created this implicitly from questions answered by users and other data sources. Our thesis is that for many activities -- for example deciding what movie to see or blouse to buy -- it's more useful to have the neighbors on your graph be people with similar tastes versus people who are your friends. I'll talk about the rising importance of these types of graphs, and dig deeper into some examples.

Chris Dixon is the co-founder of Hunch and the Founder Collective and a personal investor in a number of early-stage technology companies. He lives and works in New York city.

This Tech Talk was presented at one of the Google NYC Tech Talk Series. For more information, or to attend future events at the Google NYC Engineering Office, see http://www.meetup.com/google-nyc-tech-talks/