MSc Thesis Proposal "Temporal Graph Convolutional Network for Implicit Relation Prediction: Leveraging Timestamps and Confidence" By: Lida Mirzaei

Temporal Graph Convolutional Network for Implicit Relation Prediction: Leveraging Timestamps and Confidence
MSc Thesis Proposal by:
Lida Mirzaei

Date: Wednesday, January 31, 2024
Time: 1:00 pm – 2:00 pm
Location: Essex Hall Room 122

Abstract:
In the dynamic landscape of social network analysis, the accurate prediction of implicit relationships presents a pivotal challenge. This paper introduces an innovative solution, the Relation Temporal Graph Convolutional Network with Confidence (R-CTGCN), specifically designed to address the intricate task of predicting implicit relations within evolving social networks. R-CTGCN unifies timestamp temporal embeddings, confidence metrics, and polynomial features within a comprehensive graph neural network framework, aiming to capture the evolving dynamics of networks and enhance predictive accuracy. Experimental evaluations conducted on diverse datasets, including Opinions and Enron, showcase R-CTGCN’s superior performance compared to both baseline models and contemporary state-of-the-art methods. The emphasis on the roles of confidence and polynomial features underscores their significance in implicit relationship prediction. The outcomes contribute substantively to the understanding of predicting implicit relationships, positioning R-CTGCN as a robust tool tailored for complex social network scenarios.

Thesis Committee:
Internal Reader: Dr. Dan Wu
External Reader: Dr. Mohammad Hassanzadeh
Advisor: Dr. Ziad Kobti