Lithium- The Science of Influence

Let’s do a live experiment
on collaboration!
Michael Wu, PhD (mich8elwu)
Principal Scientist of Analytics

June 19th, 2011

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

#e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD 2


 SNA basics
 influencer
identification
 internal
collaboration
 tools & analysis

what is a social network?
▪ social network =
• collection of entities
+ relationship among them

▪ entities = people
• SNA: nodes, vertices

▪ relationship =
• friendship (Facebook)
• colleagues (LinkedIn)
• kinship, communication, etc.
• SNA: edges, connections

Enterprise 2.0 Boston #e2exp | tw: mich8elwu

what is a social graph?
▪ 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.


a hypothetical example
▪ I have 7 friends Joe Doug
• colleagues @ Lithium
Joe + Phil who are also colleagues
• @ UC Berkeley
Jack + Ryan Phil me Adam
• @ Los Alamos Nat’l Lab
Don + Ryan
• Ryan & I overlap @ 2 jobs
• we both worked for Jack + Don
• but Jack + Don are not colleagues Jack Don
▪ LinkedIn social graph
• relationship = coworkers Ryan


▪ my drinking buddies Joe Doug
• Doug, Adam + Ryan
• Doug + Ryan don’t get alone,
so they never go out together.
Phil me Adam
• Phil + Jack are drinking buddies
too, but I never gone drinking with
either of them because they are
the big bosses.
Jack Don
▪ beer buddy graph
• relationship = drink beer together Ryan


▪ I love badminton Joe Doug
• Joe @ Lithium
Jack @ UC Berkeley
Don @ Los Alamos
• Ryan also plays, Phil me
and has play with Phil + Doug.
Adam
• But they are pros and play each
other in tournaments, so we’ve
never played them

▪ badminton pal graph Jack Don
• relationship = have played
badminton with each other Ryan


▪ I just created 3 social graph Joe Doug
from my social network
▪ I can also create another:
the Facebook social Phil me Adam
graph
▪ by specifying what
relationship the edges Jack Don
represent, we can get very
different graphs
Ryan


what is a social network analysis (SNA)?
▪ SNA = 1
4
1
3
1 1
• construction of social graphs 4
that contains the relevant 1
3 4 1
relationship
2
• the analysis of social graphs 3
by computing network metrics 5 3 7
on nodes (and edges too) 2
• Example: degree centrality 4 2
3
4 3 2
• interpreting the network 3
metrics to gaining insights + 1
1
intelligence about the social 2 2
2 3 5
network


reading a social graph

▪ 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



 SNA basics
 influencer
identification
 internal
collaboration

“Despite the wealth of data generated
on social media, no one has data on
who actually influenced who
”
We need a model!


a model for influence propagation
influencer
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
target: with respect to his information needs
influencee

the importance of relevance and timing

friendship

relevant relationship
FanGirl
WizKid
w/in 1 month
1 month ago
3 month ago
6 month ago
PopGuy


constructing an unweighted influence graph
adjacency matrix representation
b degree
d a b c d e f g h i j k centrality
c a 0 1 1 1 1 0 0 1 0 1 0 sum 6
b 1 0 0 0 0 1 0 0 1 0 0 3
a f c 1 0 0 0 1 0 0 0 0 0 0 2
e d 1 0 0 0 0 1 0 0 0 0 0 2
e 1 0 1 0 0 0 1 0 0 0 0 3
j f 0 1 0 1 0 0 1 1 0 1 1 6
g g 0 0 0 0 1 1 0 1 0 0 1 4
h h 1 0 0 0 0 1 1 0 1 0 1 5
i i 0 1 0 0 0 0 0 1 0 1 1 4
j 1 0 0 0 0 1 0 0 1 0 0 3
k k 0 0 0 0 0 1 1 1 1 0 0 4


eigenvector centrality & Google’s PageRank
▪ how does Google find the 2 2
2 2 2
most authoritative web
2 2 2
pages on the WWW?
2 2 2
▪ WWW = web pages 2 2 2 22
2
+ hyperlinks between them
2 2 2 2 22
2 2 2 2
▪ PageRank  authoritative 2 2
web pages 2 2
2

eigenvector centrality ~ Google’s PageRank
▪ mathematically, this is the 2 2
2 2 2
same problem as finding
2 2 2
influential users in the
2 2 2
community
2 2 2 22
▪ web pages  users 2
2 2 2 2 22
▪ hyperlink  2 2 2 2
communication + 2 2
2 2
interactions
2

eigenvector centrality ~ Google’s PageRank
▪ # = connections
• Only ≥ 10 are labeled
12 29
▪ who is most 11
authoritative?
12 18

10 32

11


betweenness centrality
▪ # = connections
• Only ≥ 10 are labeled
12 29
▪ who is most 11
authoritative?
▪ connector, bridge, 12 18

boundary spanner,
gate keeper, innovator, 10 32
hidden influencers, …
11


▪ a real social graph
of a community w/
4 sub-communities

▪ they are all
connected by a
single network
bridge (with only
10 connections)



 SNA basics
 influencer
identification
 internal
collaboration

tug o’ war
▪ relevant relationship ▪ data you can get
• collaborated on some project • communication: emails, IMs, phone
• produced some products/services calls, sms messages, etc.
together • meetings: calendar data
• co-authored, co-created, or co- • content usage: downloads, edits, or
designed something sharing of content by someone else


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



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



▪ 1 IM session/week

▪ 1 meeting/month
• >3 meetings/month
• >5 meetings/month


▪ 1 eMail exchange/day CEO
marketing database
PR guy

▪ 1 IM session/week Sales + + = PM
• >5 sessions/week Rep1 = collaborated

▪ 1 meeting/month
• >3 meetings/month Java
Sales developer
• >5 meetings/month Rep2
accounts/finance

▪ Collaboration means different CEO
marketing database
things for different roles PR guy
• For product team:
lots of IMs and long email threads
• For executives & managers:
lot of meetings together Sales PM
• Email (or any single data source) Rep1
is usually not a good indicator of
collaboration. People could email
simply b/c they are friends
Java
5 emails w/ >5 replies Sales developer
>10 IM sessions/week Rep2
>5 meetings/month accounts/finance

in summary
▪ you must define what 1
4
1
3
1 1
collaboration means in 4
1
terms of the data you 3 4 1
can get before you can 2
quantify collaboration 3
5 3 7
▪ then we can construct 2
2
4
the collaboration graph 3
3
4 2
▪ compute network metrics 1
3

& quantify collaboration 1
2 5 2 2
3



 SNA basics
 influencer
identification
 internal
collaboration

SNA tools and libraries
▪ Open source SNA tools ▪ Open source SNA libraries

▪ C++

scale / power
• moderate scale: ~millions of nodes
ease of use

• many algorithms

▪ Java
Pajek • very large scale
10s−100M nodes
• few metrics



Analysis of the live
experiment



Thank you
Q&A + discussion


Lithium- The Science of Influence

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