Jacob Ortiz

Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Katerina Potika

Second Advisor

Nada Attar

Third Advisor

William Andreopoulos


opinion maximization, information diffusion, opinion dy- namics, adaptive bounded-confidence, attraction, repulsion, clustering coefficient, centralities


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.

Available for download on Sunday, May 25, 2025