Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks
This work addresses predicting spurious relationships in social networks, but it appears incremental as it compares existing models without introducing new methods.
The paper tackled predicting unconnected relationships in signed social networks by comparing Triadic, Latent Information, and Sentiment models, finding they outperform random models and complement each other in different cases.
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.