Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Katerina Potika
Second Advisor
Nada Attar
Third Advisor
William Andreopoulos
Keywords
opinion maximization, information diffusion, opinion dy- namics, adaptive bounded-confidence, attraction, repulsion, clustering coefficient, centralities
Abstract
Social networks have become a significant source of information due to their easy accessibility, low cost, and ability to spread information quickly. Opinions are crucial in shaping our communication and decision-making processes, and our social connections significantly influence them. We model opinions as continuous values from 0 to 1, i.e., 0 means strong disagree and 1 means strong agree. Each agent has an initial opinion as well as a confidence about her opinion and through interactions with the other agents both are updated. Opinion maximization has gained popularity due to social media’s growing impact on our daily lives, where we seek to maximize the overall opinion values after all agents interact. We present a new enriched framework that we call ADAPTIVE, which is a bounded-confidence model, on how opinions spread dynamically by incorporating various factors, such as heterogeneous confidence between agents, updating confidences using attraction or repulsion, and network rewiring. We then propose an algorithm for the opinion maximization problem, considering the network structure to select seed nodes, such as centralities and triangles. We use our proposed model to determine the maximum overall opinion. Through our experiments in one real-world graph and one synthetic graph we show that although our model is richer than the baseline the time to converge is comparable to the baseline. Our algorithm performs similarly and sometimes marginally better than other heuristics when it comes to maximizing the overall opinions.
Recommended Citation
Ortiz, Jacob, "Adaptive bounded-confidence model for opinion maximization" (2024). Master's Projects. 1396.
DOI: https://doi.org/10.31979/etd.rmwc-8wjy
https://scholarworks.sjsu.edu/etd_projects/1396
