What Is the Added Value of Negative Links in Online Social Networks?
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What Is the Added Value of Negative Links in Online Social Networks?
Jérôme Kunegis, Julia Preusse, Felix SchwagereitInstitute for Web Science and Technologies (WeST), University of Koblenz–Landau
We thank Paolo Massa for providing the Epinions dataset. The research leading to these results has received funding from the European Community's Seventh Framework Programme under grant agreement n° 257859, ROBUST.
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Social Networks: Ties between People
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Some People Don't Like Each Other
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Real Social Networks Contain Negative Ties
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Online Social Networks Have Many Forms of Positive Links
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Where Are the Negative Ties?
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They Are on Slashdot.org
79,120 users; 392,326 positive ties; 123,255 negative ties
http://konect.uni-koblenz.de/test/networks/slashdot-zoo
[1] J. Kunegis, A. Lommatzsch, C. Bauckhage. The Slashdot Zoo: Mining a Social Network with Negative Edges. In Proc. WWW, pages 741– 750, 2009.
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And Epinions.com
131,828 users; 717,667 positive ties; 123,705 negative ties
http://konect.uni-koblenz.de/networks/epinions
[2] P. Massa, P. Avesani. Controversial users demand local trust metrics: an experimental study on epinions.com community. In Proc. AAAI, pages 121– 126, 2005.
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What Is the Added Value of Negative Links?
Pro: More information about the community
Con: More negativity in the community
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Pro: More information about the community
Really?
What Is the Added Value of Negative Links?
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Our Idea
If negative links can be predicted from positive ones, then
negative links do not bring any added
value to the network
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Machine Learning Task
Prediction
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Related Problem: Prediction of Future Ties (Link Prediction)
Prediction
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Indicators for Link Prediction
?Neighborhood-basedCentrality-based
➔Common neighbors➔Cosine similarity➔Jaccard coefficient➔Adamic–Adar➔Exponential/Neuman graph kernel➔Paths of length 3
➔Degree➔PageRank
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Centrality-based Indicators
DegreePageRank
u v
f(u, v) = d(u) d(v)f(u, v) = PR(u) PR(v)
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Neighborhood-based Indicators
Common neighborsJaccard indexCosine similarity
A B
u v
f(u, v) = |A Ո B|f(u, v) = |A Ո B| / |A U B|f(u, v) = |A Ո B| / sqrt(|A| |B|)
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Link Prediction: Edge vs No Edge
?tie
no tie
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Our Task: Positive Edge vs Negative Edge vs No Edge
?pos. tie
neg. tie
no tie
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A Good and Simple Idea Which Does Not Work
?
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Learning Feature Weights by Regression
Dataset Degree PageRank Common neighbors
Paths of length 3
Cosine similarity
Slashdot 0.2502 0.2321 −0.5411 −0.4866 −3.9434
Epinions −0.0105 0.8498 −0.8587 −0.3827 −5.0360
Centrality-based Neighborhood-based
Preferential attachmentholds for
negative edges
Conflictis avoided
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Measuring Prediction Accuracy
(1) Neg-tie vs Pos-tie + No-tie
(2) Neg-tie vs No-tie
(3) Neg-tie vs Pos-tie
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Prediction Accuracy
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Let's Look at the Data
Slashdot Epinions
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Combine PageRank and Cosine Similarity
f = ® ({ ) − ̄ log(cos)log(PR) if cos = 0min(log(PR)) otherwise
Slashdot Epinions
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Evaluation
All >0.5
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How Much Better Does It Get If We Have Negative Links?
The “enemy” function adds 0.05 points
of AUC to knowing negative ties
http://konect.uni-koblenz.de/
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