Negative Link Prediction in Social Mediatangjili/publication/Negative-Link_prediction... ·...
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Negative Link Prediction in Social Media WSDM2015 1
Negative Link Prediction in Social Media
Jiliang Tang*, Shiyu Chang #, Charu Aggarwalⱡ and Huan Liu* *Data Mining and Machine Learning Lab, Arizona State University
#Beckman Institute, University of Illinois at Urbana-Champaign ⱡ IBM T.J. Watson Research Center
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Motivation
Negative Link Prediction
Unavailability Negative links are unwanted properties in online worlds Most social media sites allow positive links
― Friendships in Facebook ― Following in Twitter
Few social media sites allow negative links
Importance Negative links could be as important as positive links
Negative links have added value over positive links Negative links can help various online applications
—Positive link prediction —Recommendation
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Prevalently Available Sources in Social Media
Users:
Positive links:
Posting:
Interacting:
Content:
a b c d e
a 0 1 1 0 0
b 0 0 1 1 0
c 0 0 0 0 1
d 0 0 0 0 1
e 0 0 0 1 0
1 2 3 4
a 1 0 0 0
b 1 0 0 0
c 0 0 1 0
d 0 0 0 0
e 0 0 0 1
1 2 3 4
a 0 0 0 0
b 1 1 0 0
c 1 1 0 1
d 0 0 0 -1
e 0 0 -1 0
Positive Links Ap Authorship Matrix A Opinion Matrix O
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Problem Statement
a b c d e
a 0 1 1 0 0
b 0 0 1 1 0
c 0 0 0 0 1
d 0 0 0 0 1
e 0 0 0 1 0
1 2 3 4
a 1 0 0 0
b 1 0 0 0
c 0 0 1 0
d 0 0 0 0
e 0 0 0 1
1 2 3 4
a 0 0 0 0
b 1 1 0 0
c 1 1 0 1
d 0 0 0 -1
e 0 0 -1 0
Positive Links Ap Authorship Matrix A Opinion Matrix O
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Datasets
Epinions
– Trust and distrust links
Slashdot
– Friend and foe links
Reviews
Blogs
Writing Rating
Posting Replying/Commenting
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Where are our ``Foes’’?
We examine the typical structural relationships of “foes” within the positive network
– More than 45% within 2 hops
– More than 80% within 3 hops
1 2
3
5 4
6
2
Inf
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Social Theories
Balance Theory
– 92.31% and 93.01% triads in Epinions and Slashdot are balanced, respectively
Status Theory
– 94.73% and 93.38% of triads in Epinions and Slashdot satisfy status theory, respectively
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Negative Links and Negative Interactions
Negative links are positively correlated to negative
interactions
The more negative interactions two users have, the more likely a negative link exists between them
The random ratio is 2.4177e-04 and 3.9402e-04 in Epinions and Slashdot, respectively
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Our Findings
Most of our “foes” are close to us within a few (e.g.,2 or 3) hops in the positive network
Most of triads in signed networks satisfy balance theory and status theory
Pairs of users with negative interactions are more likely to have negative links than those without
Negative interactions between users increase the propensity of negative links
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A Classification Solution
Positive Negative and Missing
We consider the negative link prediction problem as a classification problem
– Missing links as positive samples PS
– Negative links as negative samples NS
Since missing and negative links are mixed together, it is challenging to construct labels
– Randomly select pairs as PS
– How to construct NS?
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Negative Sample Construction
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Considerations about the Basic Classifier
The classifier should be noise-tolerant
– Positive and negative samples contain noisy labels
We are able to capture reliability weights of samples
– Samples have different degrees of reliability
We are able to model balance theory
– Maintaining or increasing the structural balance with predicted negative links
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Capturing Reliability
The noise-tolerant SVM for the negative link prediction problem
Capturing reliability
– Positive samples > negative samples
– More negative interactions > less negative interactions
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Modeling Balance Theory
(u,v) have a positive link and w does not have positive links with both u and v
– If we want to maintain the structural balance, we can predict (u,w) and (v,w) as missing links
– If we want to increase the structural balance, we can predict (u,w) and (v,w) as negative links
u
v
w
u
v
w
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The Proposed Framework NeLP
NeLP is to solve the following optimization problem
Reliability of training
samples
Balance theory
Correlation and status theory
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Experimental Questions
Can negative links be predicted by the proposed framework NeLP?
How do various model components of NeLP contribute to the performance?
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Quality of Predicted Negative Links
Status theory can improve the performance of negative link prediction
The proposed framework NeLP can accurately predict negative links
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Component Analysis
We can perform component analysis by controlling their corresponding parameters
– Cn: negative samples
– cj: the j-th negative samples
– Cb: balance theory
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Future Work
The availability of negative links allow various social media applications
– Positive link prediction
– Recommendation
– Classification and clustering
We will investigate frameworks with other pervasively available sources
– User-generated content
– Cross media data
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Acknowledgements
Members of Data Mining and Machine Learning Lab at ASU
Funding Agencies: Army Research Office , The Office of Naval
Research and the Army Research Laboratory