# Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks

arXiv: https://arxiv.org/abs/2110.09111 · HTML: https://mlsanity.com/paper/2110.09111/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Exploiting Social Network Structure for Person-to-Person Sentiment Analysis](https://mlsanity.com/paper/1409.2450/) (2014)
- [Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction](https://mlsanity.com/paper/1309.6813/) (2013)
- ["You're Mr. Lebowski, I'm the Dude": Inducing Address Term Formality in Signed Social Networks](https://mlsanity.com/paper/1411.4351/) (2014)

## Abstract (arXiv)

In the age of digital interaction, person-to-person relationships existing on social media may be different from the very same interactions that exist offline. Examining potential or spurious relationships between members in a social network is a fertile area of research for computer scientists -- here we examine how relationships can be predicted between two unconnected people in a social network by using area under Precison-Recall curve and ROC. Modeling the social network as a signed graph, we compare Triadic model,Latent Information model and Sentiment model and use them to predict peer to peer interactions, first using a plain signed network, and second using a signed network with comments as context. We see that our models are much better than random model and could complement each other in different cases.