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MSc Thesis Proposal Announcement by Shikhar Sanghvi:"A Feature Based Model for Negative Sign Prediction in Signed Social Networks"

Thursday, June 4, 2020 - 13:00 to 14:00
 

SCHOOL OF COMPUTER SCIENCE

The School of Computer Science is pleased to present…

MSc Thesis Proposal by: Shikhar Sanghvi 

 
Date: Thursday June 4, 2020
Time:  1:00pm – 2:00pm
 
Abstract: 
People hold all kinds of positive and negative feelings against one another. Social networking online serves as a platform for showcasing such relationships, whether friendly or unfriendly, like or dislike, trust or distrust, cooperation or dissension. These types of interactions result in the creation of signed social networks (SSNs). Although negative signs typically dominate the final user decision in most real applications, it cannot be directly propagated between users like positive signs. The study on negative sign prediction is still in its early stages. There is a difference between the value of negative signs and the availability of these links in real data sets. It is therefore normal to analyze whether one can automatically predict negative signs from the widely available social network data. In this paper, we propose a novel negative sign prediction model which includes negative sign related features from various categories to predict negative sign in signed social network. An extensive set of experiments is carried out on real-world social network datasets which demonstrate that the proposed model outperforms the existing method in predicting negative signs in terms of accuracy and F1 score by 4% and 14% respectively.
 
Keywords: Negative sign prediction, signed social networks, link prediction

 

Thesis Committee: 

Internal Reader: Dr. Jianguo Lu
External Reader: Dr. Fazle Baki
Advisor: Dr. Ziad Kobti
 

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