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ACM Transactions on Information Systems (TOIS)
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CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 19th international conference on World wide web
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SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
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Proceedings of the 23rd international conference on World wide web
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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.