  We analyse 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.
