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MetaFac: Community Discovery via Relational Hypergraph Factorization. Tracking Multiple Relations in Social Media. Yu-Ru Lin 1 , Jimeng Sun 2 , Paul Castro 2 , Ravi Konuru 2 , Hari Sundaram 1 and Aisling Kelliher 1 1 Arts, Media and Engineering, Arizona State University

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metafac community discovery via relational hypergraph factorization

MetaFac: Community Discovery via Relational Hypergraph Factorization

Tracking Multiple Relations in Social Media

Yu-Ru Lin1, Jimeng Sun2, Paul Castro2,

Ravi Konuru2, Hari Sundaram1 and Aisling Kelliher1

1Arts, Media and Engineering, Arizona State University

2IBM T.J. Watson Research Center

slide3

raustin

What does s/he like?

slide4

How to model multi-relational social data?

(Q1)

following

tweets

Favorites

History

Dugg

Comments

Favorites

Friends

raustin

slide5

How to model multi-relational social data?

How to reveal communities consistent across multi-relations?

How to track these communities over time?

(Q1)

(Q3)

(Q2)

following

tweets

Favorites

History

Dugg

Comments

Favorites

Friends

raustin

raustin

slide9

How to reveal communities consistent across multi-relations?

(Q2)

community := a cluster of people who interact with resource and each other in a coherent manner

slide10

pc

pi|c

j

xijk cpc∙pi|c∙pj|c∙pk|c

k

i

core tensor

Clustering as factorization

facet factors

slide11

G

core tensor

facet factors

U(1)

U(2)

U(3)

U(4)

Factorization on metagraph

slide12

Metagraphfactorization (MetaFac)

for community extraction on metagraph

core tensor

data tensor

facet factors

objective function

cost(G)= D((r)||[z] mU(m))

rE

m:v(m)~e(r)

KL divergence

z, {U} can be solved with linear time complexity

slide14

Metagraphfactorization

for Time evolving data (MFT)

t-1

objective function

cost(G) =  D((r)||[z] mU(m))

cost(G) = (1-)

t

t

t

+  {D(zt-1||z)+ D(Ut-1(q)||U(q))}

temporal cost

slide16

Dataset: Digg

5 facets, 6 relations

time span:

3 weeks in Aug 2008

slide17

Community analysis

C1: gamming industry news

C2: US election news

C4: general political news

C3: world news

Change in community size

Change in community keywords

slide18

Prediction performance

Digg prediction

Comment prediction

slide20

Problem:

How to track communities in dynamic multi-relational data?

Approach:

MetaFacfor community extraction on metagraph

Results:

meaningful mining results and best prediction quality

thanks

Code / data – available online:

http://www.public.asu.edu/~ylin56/kdd09sup.html

Questions? Suggestions?

[email protected]

Thanks!
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