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We analyse the corpus of user relationships of the Slashdot technology newssite. The data was collected from the Slashdot Zoo feature where users of thewebsite can tag other users as friends and foes, providing positive andnegative endorsements. We adapt social network analysis techniques to theproblem of negative edge weights. In particular, we consider signed variants ofglobal network characteristics such as the clustering coefficient, node-levelcharacteristics such as centrality and popularity measures, and link-levelcharacteristics such as distances and similarity measures. We evaluate thesemeasures on the task of identifying unpopular users, as well as on the task ofpredicting the sign of links and show that the network exhibits multiplicativetransitivity which allows algebraic methods based on matrix multiplication tobe used. We compare our methods to traditional methods which are only suitablefor positively weighted edges.

The Slashdot Zoo: Mining a Social Network with Negative Edges