Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks

• URL: http://arxiv.org/abs/2110.09111v1
• Date: Mon, 18 Oct 2021 09:03:18 GMT
• Title: Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks
• Authors: Zhihao Wu, Taoran Li, Ray Roman
• Abstract summary: 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.
• Score: 0.666659730119789
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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