Abstract

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. Errata: - Section 2 paragraph 6: The missing reference should be replaced by "On the Notion of Balance of a Signed Graph", Frank Harary, Mich. Math 2, 1963. - Section 3, paragraph 4: The dataset was actually extracted between May 2008 and February 2009. - In Equations (1) and (2), ||bar A^2||_+ should be replaced with ||bar A^2||_+ − 2m, where m is the edge count.

@inproceedings{kunegis2009a,
	author = {Jérôme Kunegis and Andreas Lommatzsch and Christian Bauckhage},
	title = {The {Slashdot Zoo}:  Mining a Social Network with Negative Edges},
	year = {2009},
	booktitle = {Proc. Int. World Wide Web Conf.},
	pages = {741--750},	
}