DIFFUSION MODELS OVER DYNAMIC NETWORKS USINGTRANSFORMERS

In my thesis, I propose a Graph Regularized-Attention-Based Diffusion Transformer (GRAD-T) model, which uses kernel temporal attention and a regularized sparse graph method to analyze model general diffusion processes over networks. The proposed model uses the spatiotemporal nature of data generated from diffusion processes over networks to examine phenomena that vary across different locations and time, such as disease outbreaks, climate patterns, ecological changes, information flows, news contagion, transportation flows or information and sentiment contagion over social networks. The kernel attention models the temporal dependence of diffusion processes within locations, and the regularized spatial attention mechanism accounts for the spatial diffusion process. The proposed regularization using a combination of penalized matrix estimation and a resampling approach helps in modeling high-dimensional data from large graphical networks, and identify the dominant diffusion pathways. I use the model to predict how emotions spread across sparse networks. I applied the model to a unique dataset of COVID-19 tweets that I curated, spanning April to July 2020 across various U.S. locations. I used model parameters (attention measures) to create indices for comparing emotion diffusion potential within and between nodes. Our findings show that negative emotions like fear, anger, and disgust demonstrate substantial potential for temporal and spatial diffusion. Using the dataset and the proposed method we demonstrate that different types of emotions exhibit different patters of temporal and spatial diffusion. I show that the proposed model improves prediction accuracy of emotion diffusion over social medial networks over standard models such as LSTM and CNN methods. Our key contribution is the regularized graph transformer using a penalty and a resampling approach to enhance the robustness, interpretability, and scalability of sparse graph learning.