NDLI: 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?

Content Provider	ACM Digital Library
Author	Schwagereit, Felix Preusse, Julia Kunegis, Jérôme
Abstract	We investigate the "negative link" feature of social networks that allows users to tag other users as foes or as distrusted in addition to the usual friend and trusted links. To answer the question whether negative links have an added value for an online social network, we investigate the machine learning problem of predicting the negative links of such a network using only the positive links as a basis, with the idea that if this problem can be solved with high accuracy, then the "negative link" feature is redundant. In doing so, we also present a general methodology for assessing the added value of any new link type in online social networks. Our evaluation is performed on two social networks that allow negative links: The technology news website Slashdot and the product review site Epinions. In experiments with these two datasets, we come to the conclusion that a combination of centrality-based and proximity-based link prediction functions can be used to predict the negative edges in the networks we analyse. We explain this result by an application of the models of preferential attachment and balance theory to our learning problem, and show that the "negative link" feature has a small but measurable added value for these social networks.
Starting Page	727
Ending Page	736
Page Count	10
File Format	PDF
ISBN	9781450320351
DOI	10.1145/2488388.2488452
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-05-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Link prediction Enmity Distrust Signed network Platform policies
Content Type	Text
Resource Type	Article

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