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Dr. Michael Wu on the Science of Influence
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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
#e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
Enterprise 2.0 Boston
SNA basics
influencer
identification
internal
collaboration
tools & analysis
3
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
a hypothetical example
Ryan
Joe Doug
me
Jack Don
Adam Phil
7
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
a hypothetical example
Ryan
Joe Doug
me
Jack Don
Adam Phil
8
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
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
a hypothetical example
9
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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)?
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Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
#e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
Enterprise 2.0 Boston
SNA basics
influencer
identification
internal
collaboration
tools & analysis
12
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
Despite the wealth of data generated
on social media, no one has data on
who actually influenced who
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We need a model!
“ ”
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
constructing an unweighted influence graph
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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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
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2
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2 2
2
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2 2
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2 2
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2 2
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2 2
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Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
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2 2
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2 2
2
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2 2
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2 2
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2 2
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2 2
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▪ mathematically, this is the
same problem as finding
influential users in the
community
▪ web pages users
▪ hyperlink
communication +
interactions
eigenvector centrality ~ Google’s PageRank
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Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ # = connections • Only ≥ 10 are labeled
▪ who is most
authoritative?
eigenvector centrality ~ Google’s PageRank
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Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ # = connections • Only ≥ 10 are labeled
▪ who is most
authoritative?
▪ connector, bridge,
boundary spanner,
gate keeper, innovator,
hidden influencers, …
betweenness centrality
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12
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11
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Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
#e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
Enterprise 2.0 Boston
SNA basics
influencer
identification
internal
collaboration
tools & analysis
22
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ relevant relationship • collaborated on some project
• produced some products/services
together
• co-authored, co-created, or co-
designed something
tug o’ war
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▪ 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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 1 eMail exchange/day • 5 emails w/ 1 replies
• 5 emails w/ >5 replies
• 5 emails w/ >10 replies
a hypothetical example
24
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 1 eMail exchange/day • 5 emails w/ 1 replies
• 5 emails w/ >5 replies
• 5 emails w/ >10 replies
▪ 1 IM session/week • >5 sessions/week
• >10 sessions/week
a hypothetical example
25
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 1 eMail exchange/day • 5 emails w/ 1 replies
• 5 emails w/ >5 replies
• 5 emails w/ >10 replies
▪ 1 IM session/week • >5 sessions/week
• >10 sessions/week
▪ 1 meeting/month • >3 meetings/month
• >5 meetings/month
a hypothetical example
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Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 1 eMail exchange/day • 5 emails w/ 1 replies
• 5 emails w/ >5 replies
• 5 emails w/ >10 replies
▪ 1 IM session/week • >5 sessions/week
• >10 sessions/week
▪ 1 meeting/month • >3 meetings/month
• >5 meetings/month
a hypothetical example
27
CEO
marketing
PR
PM
Sales
Rep2
Java
developer
database
guy
accounts/finance
Sales
Rep1
+ + = = collaborated
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
a hypothetical example
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
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
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#e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
Enterprise 2.0 Boston
SNA basics
influencer
identification
internal
collaboration
tools & analysis
30
Enterprise 2.0 Boston #e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
▪ 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
#e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
Enterprise 2.0 Boston
Analysis of the live
experiment
32
#e2exp | tw: mich8elwu
linkedin.com/in/MichaelWuPhD
Enterprise 2.0 Boston
Thank you
Q&A + discussion
33