Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks

TL;DR

This paper investigates methods to predict unconnected relationships in signed social networks, comparing models like Triadic, Latent Information, and Sentiment, using precision-recall and ROC metrics.

Contribution

It introduces a comparative analysis of different models for predicting unconnected nodes in signed networks, including context-aware modeling with comments.

Findings

Models outperform random baseline

Models complement each other in different scenarios

Context-aware models improve prediction accuracy

Abstract

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.