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What Is the Added Value of Negative Links in Online Social Networks?
| Content Provider | CiteSeerX |
|---|---|
| Author | Kunegis, Jérôme Schwagereit, Felix Preusse, Julia |
| Abstract | koblenz.de 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 learn-ing problem of predicting the negative links of such a net-work 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 combina-tion 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 applica-tion of the models of preferential attachment and balance theory to our learning problem, and show that the “nega-tive link ” feature has a small but measurable added value for these social networks. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Technology News Website Slashdot Product Review Site Epinions Machine Learn-ing Problem Positive Link Social Network General Methodology Negative Link Usual Friend Added Value Negative Link Feature Preferential Attachment Learning Problem Balance Theory New Link Type Negative Edge Nega-tive Link Feature High Accuracy Online Social Network Proximity-based Link Prediction Function |
| Content Type | Text |
