Year: 2021
Type: dissertation
Abstract: Social network analysis has been used to study groups, communities, and network dynamics.Previous studies have also identified leaders and active spreaders in the networks using network and node characteristics.However, these studies focused on a single type of social network instead of multiple types.Also, these studies are only limited to a specific scope, for example, descriptive analysis or study of some network dynamics using LMS data.A recently published different type of network dataset of daily communications of the same users, using different mediums, has motivated the researchers to analyze the multiple types of social networks.To study diffusion and influence maximization, previous studies have used one type of social network instead of multiple types.For that purpose, this study identifies the influential and diffusion capability of students to highlight their importance and role in the different types of social networks.To find the influence maximizers in students' social networks of different types, the current study presents, 1-comparison of different network dynamics and metrics of the networks, 2-identification of factors behind the formation of networks using probabilistic and inferential models, and 3-identification of influential nodes using network heuristics.This study is conducted using four different types of social network data including Calls, SMS, Facebook, and Proximity networks of the same users.The results show that Proximity and Facebook are good networks having more interaction, rich clubs, and better ties in the participants.The comparison of network measures presents good results as most of the time actual measures are more than the average of randomly generated network's statistics.The generative factors are found to be mutuality for calls and messaging networks.For Facebook and Proximity networks, shared partners are found to be the generative factor.Finally, Influential nodes are identified using important node level centrality measures.Furthermore, clusters comparison shows that most of the measures are playing a positive role in the identification of influence maximizers.
Author: Mohsin Hafiz
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3213753010