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<p dir="ltr">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.</p>

abstract: Social media has become the norm of everyone for communication. The usage of social media has increased exponentially in the last decade. The myriads of Social media services such as Facebook, Twitter, Snapchat, and Instagram etc allow people to connect with their friends, and followers freely. The attackers who try to take advantage of this situation has also increased at an exponential rate. Every social media service has its own recommender systems and user profiling algorithms. These algorithms use users current information to make different recommendations. Often the data that is formed from social media services is Linked data as each item/user is usually linked with other users/items. Recommender systems due to their ubiquitous and prominent nature are prone to several forms of attacks. One of the major form of attacks is poisoning the training set data. As recommender systems use current user/item information as the training set to make recommendations, the attacker tries to modify the training set in such a way that the recommender system would benefit the attacker or give incorrect recommendations and hence failing in its basic functionality. Most existing training set attack algorithms work with ``flat" attribute-value data which is typically assumed to be independent and identically distributed (i.i.d.). However, the i.i.d. assumption does not hold for social media data since it is inherently linked as described above. Usage of user-similarity with Graph Regularizer in morphing the training data produces best results to attacker. This thesis proves the same by demonstrating with experiments on Collaborative Filtering with multiple datasets. / Dissertation/Thesis / Masters Thesis Computer Science 2019