Thesis Defense – Sena Odabaşı (MSDS)

Sena Odabaşı – M.Sc. Data science

Asst. Prof. Dilek Günneç Danış – Advisor

Date: 12.08.2022

Time: 10:30

Location: AB1 – 241

“n-Hop Influence Maximization Problem Under Deterministic Linear Threshold Model”

Asst. Prof. Dilek Günneç Danış, Özyeğin University

Asst. Prof. Enis Kayış, Özyeğin University

Assoc. Professor Emre Keskin, Atatürk University

Abstract:

The Influence Maximization Problem (IMP) finds a set of highly influential nodes within a social network in order to maximize the spread of influence. We consider that people can have an influence on their direct (1-hop), 2-hop, and 3-hop neighbors. IMP with extended influence transitivity is called n-hop IMP.

We study the problem under the deterministic linear threshold model and find a heuristic solution. In our proposed heuristic model, there are two parts, extended seed set algorithm, and local search. Main purpose of extended seed set algorithm is creating a node set and send it to local search which is based on trying to improve it via replacing the nodes in the given set. We used two different features for selecting the candidate nodes. We find an equation to estimate the value of a node set without actually computing. We used real-life and synthetic networks to test our solution method and generated weight and threshold values in three different methods.

Bio:

Sena Odabaşı graduated from Haydarpaşa Anatolian High School in 2014 and obtained B.Sc. in Computer Science from Özyeğin University in February 2019 at top of her class. She has worked in IBM as a project management officer for a year in 2019 and has been pursuing her M.Sc. degree in Data Science at Özyeğin University since September 2020 under the supervision of Asst. Prof. Dilek Günneç. She has been a teaching assistant in Programming for Operation Research and Applied Statistics courses. She currently works as a part-time data scientist at KoçDigital.