lithium- the science of influence

Let’s do a live experiment

on collaboration! Michael Wu, PhD (mich8elwu)

Principal Scientist of Analytics

June 19th, 2011

#e2exp | tw: mich8elwu

linkedin.com/in/MichaelWuPhD

Enterprise 2.0 Boston

Collaborative note-taking

experiment:

can we collectively tweet, RT,

mention each other to produce a

comprehensive set of notes for

this talk

#e2exp

@mich8elwu

2

http://www.tweetdeck.com/

http://hootsuite.com/

https://seesmic.com/




SNA basics

influencer

identification

internal

collaboration

tools & analysis

3

Enterprise 2.0 Boston #e2exp | tw: mich8elwu


▪ social network = • collection of entities

+ relationship among them

▪ entities = people • SNA: nodes, vertices

▪ relationship = • friendship (Facebook)

• colleagues (LinkedIn)

• kinship, communication, etc.

• SNA: edges, connections

what is a social network?

4



▪ social graph = • a diagram consist of nodes +

edges that represents the

social network

▪ key: 1 social network

can have many social

graph

▪ my social network = • my friends

+ my colleagues

+ my relatives etc.

what is a social graph?

5



▪ I have 7 friends • colleagues @ Lithium

Joe + Phil who are also colleagues

• @ UC Berkeley

Jack + Ryan

• @ Los Alamos Nat’l Lab

Don + Ryan

• Ryan & I overlap @ 2 jobs

• we both worked for Jack + Don

• but Jack + Don are not colleagues

▪ LinkedIn social graph • relationship = coworkers

a hypothetical example

Ryan

Joe Doug

me

Jack Don

Adam Phil

6



▪ my drinking buddies • Doug, Adam + Ryan

• Doug + Ryan don’t get alone,

so they never go out together.

• Phil + Jack are drinking buddies

too, but I never gone drinking with

either of them because they are

the big bosses.

▪ beer buddy graph • relationship = drink beer together


Ryan

Joe Doug

me

Jack Don

Adam Phil

7



▪ I love badminton • Joe @ Lithium

Jack @ UC Berkeley

Don @ Los Alamos

• Ryan also plays,

and has play with Phil + Doug.

• But they are pros and play each

other in tournaments, so we’ve

never played them

▪ badminton pal graph • relationship = have played

badminton with each other


Ryan

Joe Doug

me

Jack Don

Adam Phil

8



Ryan

Joe Doug

me

Jack Don

Adam Phil

Ryan

Joe Doug

me

Jack Don

Adam Phil

▪ I just created 3 social graph

from my social network

▪ I can also create another:

the Facebook social

graph

▪ by specifying what

relationship the edges

represent, we can get very

different graphs


9



▪ SNA = • construction of social graphs

that contains the relevant

relationship

• the analysis of social graphs

by computing network metrics

on nodes (and edges too)

• Example: degree centrality

• interpreting the network

metrics to gaining insights +

intelligence about the social

network

what is a social network analysis (SNA)?

10

7

1 4

4

2

1

4

1 1

3

3

3

2

1

2

1

5 3

4

1

2

3

3

1

4 3

5

3

2

2

2



▪ most important thing when reading a social graph is to find

out what relationships are being represented by the edges

▪ do not try to make any inference or conclusion based on a

graph about anything that is not explicitly represented by the

edges

reading a social graph

11




SNA basics

influencer

identification

internal

collaboration

tools & analysis

12



Despite the wealth of data generated

on social media, no one has data on

who actually influenced who

13

We need a model!

“ ”



a model for influence propagation

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Domain Credibility: the influencer's expertise in a specific

domain of knowledge

High Bandwidth: the influencer's ability to transmit his expert

knowledge through a social media channel

Content Relevance: how closely the target's information

needs coincide with the influencer's expertise

Timing: the ability of the influencer to deliver his expert

knowledge to the target at the time when the target needed it

Channel Alignment: the amount of channel overlap between

the target and the influencer

Target Confidence: how much the target trusts the influencer

with respect to his information needs

influencer

target:

influencee



the importance of relevance and timing

15

w/in 1 month

1 month ago

3 month ago

6 month ago

PopGuy

FanGirl WizKid

friendship

relevant relationship



constructing an unweighted influence graph

16

a b c d e f g h i j k

a

b

c

d

e

f

g

h

i

j

k

adjacency matrix representation

1 0 0 0 0 1 0 0 1 0 0

1 0 0 0 1 0 0 0 0 0 0

1 0 0 0 0 1 0 0 0 0 0

1 0 1 0 0 0 1 0 0 0 0

0 0 0 0 1 1 0 1 0 0 1

1 0 0 0 0 1 1 0 1 0 1

0 1 0 0 0 0 0 1 0 1 1

1 0 0 0 0 1 0 0 1 0 0

0 0 0 0 0 1 1 1 1 0 0

a

b

c

d

e

f

g h

k

i

j

0 1 1 1 1 0 0 1 0 1 0

0 1 0 1 0 0 1 1 0 1 1

degree

centrality

3

2

2

3

6

4

5

4

3

4

sum 6



▪ how does Google find the

most authoritative web

pages on the WWW?

▪ WWW = web pages

+ hyperlinks between them

▪ PageRank authoritative

web pages

eigenvector centrality & Google’s PageRank

17

2

2

2 2

2

2

2

2

2 2

2

2

2 2

2

2

2

2

2

2 2

2

2

2 2

2

2

2

2 2

2

2



2

2

2 2

2

2

2

2

2 2

2

2

2 2

2

2

2

2

2

2 2

2

2

2 2

2

2

2

2 2

2

2

▪ mathematically, this is the

same problem as finding

influential users in the

community

▪ web pages users

▪ hyperlink

communication +

interactions

eigenvector centrality ~ Google’s PageRank

18



▪ # = connections • Only ≥ 10 are labeled

▪ who is most

authoritative?

eigenvector centrality ~ Google’s PageRank

19

29

32

18

12

12

11

11

10



▪ # = connections • Only ≥ 10 are labeled

▪ who is most

authoritative?

▪ connector, bridge,

boundary spanner,

gate keeper, innovator,

hidden influencers, …

betweenness centrality

20

29

32

18

12

12

11

11

10



▪ a real social graph

of a community w/

4 sub-communities

▪ they are all

connected by a

single network

bridge (with only

10 connections)

21




SNA basics

influencer

identification

internal

collaboration

tools & analysis

22



▪ relevant relationship • collaborated on some project

• produced some products/services

together

• co-authored, co-created, or co-

designed something

tug o’ war

23

▪ data you can get • communication: emails, IMs, phone

calls, sms messages, etc.

• meetings: calendar data

• content usage: downloads, edits, or

sharing of content by someone else



▪ 1 eMail exchange/day • 5 emails w/ 1 replies

• 5 emails w/ >5 replies



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▪ 1 IM session/week • >5 sessions/week

• >10 sessions/week


25








▪ 1 meeting/month • >3 meetings/month

• >5 meetings/month


26








▪ 1 meeting/month • >3 meetings/month

• >5 meetings/month


27

CEO

marketing

PR

PM

Sales

Rep2

Java

developer

database

guy

accounts/finance

Sales

Rep1

+ + = = collaborated



▪ Collaboration means different

things for different roles • For product team:

lots of IMs and long email threads

• For executives & managers:

lot of meetings together

• Email (or any single data source)

is usually not a good indicator of

collaboration. People could email

simply b/c they are friends


28

CEO

marketing

PR

PM

Sales

Rep2

Java

developer

database

guy

accounts/finance

Sales

Rep1

5 emails w/ >5 replies

>10 IM sessions/week

>5 meetings/month



▪ you must define what

collaboration means in

terms of the data you

can get before you can

quantify collaboration

▪ then we can construct

the collaboration graph

▪ compute network metrics

& quantify collaboration

in summary

29

7

1 4

4

2

1

4

1 1

3

3

3

2

1

2

1

5 3

4

1

2

3

3

1

4 3

5

3

2

2

2




SNA basics

influencer

identification

internal

collaboration

tools & analysis

30



▪ Open source SNA tools

SNA tools and libraries

31

▪ Open source SNA libraries

▪ C++ • moderate scale: ~millions of nodes

• many algorithms

▪ Java • very large scale

10s−100M nodes

• few metrics

Pajek

ease o

f u

se

scale

/ p

ow

er

http://gephi.org/

http://vlado.fmf.uni-lj.si/pub/networks/pajek/

http://nodexl.codeplex.com/

http://www.boost.org/

http://snap.stanford.edu/




Analysis of the live

experiment

32




Thank you

Q&A + discussion

33

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