In this era of rapid digital transformation, the intersections of technology, social dynamics, and neural network theory present fascinating investigation opportunities. This PhD thesis embarks on an in-depth exploration of these opportunities, offering a nuanced understanding of the complex layers and implications of digital social interactions and advanced neural network analysis. The heart of this work is an investigation into the behaviors and patterns within digital platforms, particularly focusing on blockchain technology and social media dynamics. The thesis meticulously studies user behavior in the volatile world of cryptocurrencies, uncovering patterns and implications of speculative bubbles and wash trading in NFTs. It further extends its analysis to social media platforms, like Reddit, TikTok, and Twitter, delving into the semantics of online discourse, the evolution of digital communities, and the dynamics of information dissemination in the age of misinformation. Complementing the study of social interactions is a deep dive into the realm of neural networks. The thesis proposes innovative approaches to represent and analyze neural network architectures, such as CNNs and ResNets, through a Multilayer Network-Based perspective. This analysis is not just theoretical but also finds practical applications in diverse fields, such as the compression of neural networks. This thesis tries to bridge the gap between the algorithmic complexity of neural networks and the human-centric dynamics of social media, offering a comprehensive view of how digital technologies influence and are influenced by human behavior. This work is not just a contribution to academic discussion but also a guide for those who want to navigate and understand the complexities of the digital age. In essence, it is a journey through the multifaceted aspects of digital interactions and neural network theory, providing valuable insights and methodologies for a deeper understanding of our interconnected digital world.
From Social Interactions To Neural Connections: Understanding Complex Systems Through The Unifying Language Of Networks / Bonifazi, Gianluca. - (2024 Mar 31).
From Social Interactions To Neural Connections: Understanding Complex Systems Through The Unifying Language Of Networks
BONIFAZI, Gianluca
2024-03-31
Abstract
In this era of rapid digital transformation, the intersections of technology, social dynamics, and neural network theory present fascinating investigation opportunities. This PhD thesis embarks on an in-depth exploration of these opportunities, offering a nuanced understanding of the complex layers and implications of digital social interactions and advanced neural network analysis. The heart of this work is an investigation into the behaviors and patterns within digital platforms, particularly focusing on blockchain technology and social media dynamics. The thesis meticulously studies user behavior in the volatile world of cryptocurrencies, uncovering patterns and implications of speculative bubbles and wash trading in NFTs. It further extends its analysis to social media platforms, like Reddit, TikTok, and Twitter, delving into the semantics of online discourse, the evolution of digital communities, and the dynamics of information dissemination in the age of misinformation. Complementing the study of social interactions is a deep dive into the realm of neural networks. The thesis proposes innovative approaches to represent and analyze neural network architectures, such as CNNs and ResNets, through a Multilayer Network-Based perspective. This analysis is not just theoretical but also finds practical applications in diverse fields, such as the compression of neural networks. This thesis tries to bridge the gap between the algorithmic complexity of neural networks and the human-centric dynamics of social media, offering a comprehensive view of how digital technologies influence and are influenced by human behavior. This work is not just a contribution to academic discussion but also a guide for those who want to navigate and understand the complexities of the digital age. In essence, it is a journey through the multifaceted aspects of digital interactions and neural network theory, providing valuable insights and methodologies for a deeper understanding of our interconnected digital world. I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
