Edge color constrained influence maximization in social networks
Date of Issue2017
School of Computer Science and Engineering
In this Final Year Project, we analyze the novel problem for jointly finding the top-k seed nodes and top-r most relevant topics that maximize the influence within a given target set in a social network. Our problem has direct application in viral marketing because social networks are the wide and fast medium of information-spread. A brand can maximize its promotion on social media by choosing the most influential people in a social network (seed nodes) to begin its marketing. However, simply selecting the most influential people does not maximize the spread as the content of a post dictates how likely a user will ‘share’ it. Thus, we show that it is not only imperative to select the top-k seed nodes but also the top-r most relevant topics in the network. Previous research for influence maximization largely models social networks as a graph with fixed edge probabilities of association between two users. However, real-world graphs are uncertain and the edge probabilities depends on external conditions e.g. the presence of a hashtag or keyword in a post dictates the probability of a user sharing a post rather than simply the association between two users. The problem under this model is shown to be NP-hard. Thus, we develop a system TINTO that provides an iterative algorithm targeting both efficiency and accuracy. We first solve the problem for finding the most relevant topics between a source set and target set in a social network through our maximum reliability algorithm, and then integrate it with existing algorithms to find the most influential seed nodes- thereby, forming our TINTO system. The efficiency and accuracy of the TINTO system is also demonstrated through a case study in real-world dataset.
DRNTU::Engineering::Computer science and engineering
Final Year Project (FYP)
Nanyang Technological University