# The Slashdot Zoo: Mining a Social Network with Negative Edges

arXiv: https://arxiv.org/abs/1710.11395 · HTML: https://mlsanity.com/paper/1710.11395/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 12 of 12)

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- [Signed Graph Neural Networks: A Frequency Perspective](https://mlsanity.com/paper/2208.07323/) (2022)
- [Signed Directed Graph Contrastive Learning with Laplacian Augmentation](https://mlsanity.com/paper/2301.05163/) (2023)
- [MUSE: Multi-faceted Attention for Signed Network Embedding](https://mlsanity.com/paper/2104.14449/) (2021)
- [Structural Balance and Random Walks on Complex Networks with Complex Weights](https://mlsanity.com/paper/2307.01813/) (2023)
- [Mixed membership distribution-free model](https://mlsanity.com/paper/2112.04389/) (2021)
- [A Graph Convolution for Signed Directed Graphs](https://mlsanity.com/paper/2208.11511/) (2022)
- [Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions](https://mlsanity.com/paper/2307.03077/) (2023)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social Balance](https://mlsanity.com/paper/2407.15643/) (2024)
- [Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies](https://mlsanity.com/paper/2207.09324/) (2022)
- [Defensive Alliances in Signed Networks](https://mlsanity.com/paper/2309.06801/) (2023)

## Abstract (arXiv)

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.