document: The slashdot zoo : mining a social network with negative edges We analyze the corpus of user relationships of the Slashdot technology news site . The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes , providing positive and negative endorsements . We adapt social network analysis techniques to the problem of negative edge weights . In particular , we consider signed variants of global network characteristics such as the clustering coefficient , node-level characteristics such as centrality and popularity measures , and link-level characteristics such as distances and similarity measures . We evaluate these measures on the task of identifying unpopular users , as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to be used . We compare our methods to traditional methods which are only suitable for positively weighted edges .
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