Matthew Fu

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

Chris Pollett

Third Advisor

Thomas Austin


Community Detection, Independent Cascade, Influence Maxi- mization, Quotas, Triadic Closure


Online social networks have exploded in popularity in the last decade. In addition, traditional advertising methods such as television advertising have greatly decreased. This allows companies to utilize viral marketing more effectively. With viral marketing, companies can spread information on a product to a social network by reaching out to a small group of early adopters, who will go on to inform the people around them of the product. The problem is selecting the early adopters that can maximize the spread of influence. The Influence Maximization (IM) problem is finding a social network’s most influential (early adopters) starting nodes, called seed nodes. In most models, it is assumed that a pair of users are influencing each other based on a random or uniform probability. Recently, a new algorithm was proposed that assigns these probabilities using the structure of the neighborhood, and more specifically the triadic closure. Additionally, it utilizes triadic closures to determine the most influential nodes in a dataset. However, that algorithm can be slow for large datasets. We propose the Triadic Closure Influence Maximization with Communities and Quotas (TC-IMCQ ) algorithm which utilizes communities and quotas on the number of seeds per community to reduce the computation time. Our experimental results on 20 sets show a reduced time without sacrificing much of the spread.

Available for download on Sunday, May 25, 2025