The Slashdot Zoo: Mining a Social Network with Negative Edges

TL;DR

This paper analyzes a social network with positive and negative relationships from Slashdot, adapting social network analysis techniques to handle negative edges and evaluating methods for identifying unpopular users and predicting link signs.

Contribution

It introduces adapted social network measures for signed edges and demonstrates their effectiveness in analyzing and predicting relationships in a social network with negative links.

Findings

Network exhibits multiplicative transitivity enabling algebraic analysis.

Methods outperform traditional positive-only approaches.

Effective identification of unpopular users and link sign prediction.

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.