Link Prediction using Dynamic Graph Neural Network with Application to Call Data, M.Sc. Thesis Sharif University of Technology ; Jafari Siavoshani, Mahdi (Supervisor)
Abstract
In network science, link prediction is one of the essential tasks that has been neglected. One important application of link prediction in telecommunication networks is analyzing the user's consumption pattern to provide better service. This project aims to predict future links with applications to call data using the users' call history. In previous research, there are two main approaches: 1) heuristic-based approach, and 2) deep-learning-based approach, such as graph neural networks. These methods are mainly used for processing static graphs, and therefore, we cannot generalize them to dynamic graphs. But there are many graphs which are dynamic in nature. For instance, call data records...
Cataloging briefLink Prediction using Dynamic Graph Neural Network with Application to Call Data, M.Sc. Thesis Sharif University of Technology ; Jafari Siavoshani, Mahdi (Supervisor)
Abstract
In network science, link prediction is one of the essential tasks that has been neglected. One important application of link prediction in telecommunication networks is analyzing the user's consumption pattern to provide better service. This project aims to predict future links with applications to call data using the users' call history. In previous research, there are two main approaches: 1) heuristic-based approach, and 2) deep-learning-based approach, such as graph neural networks. These methods are mainly used for processing static graphs, and therefore, we cannot generalize them to dynamic graphs. But there are many graphs which are dynamic in nature. For instance, call data records...
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