Dynamic clustering for social networks based on evolutionary computation
Author
Fariz Cheang Mohd Khairi
Date of Issue
2019School
School of Computer Science and Engineering
Abstract
Social networks are all around us and these networks are dynamic and time-evolving in nature.
However, most current research focuses solely on the a possibly complete social network or a
subset of that network. Thus, there is a lack of research in the field of dynamic network
clustering.
Dynamic network clustering requires more attention since social networks are dynamic and
any minor changes to its network alters the structure entirely. Currently, most research focuses
on the clustering of static social networks where the network is either a small subset of a huge
network structure or the entire network structure as a whole. Thus, since social networks
change over time, it is more impactful to investigate the clustering problem in the context of
dynamic social networks.
The intention of this project is to incorporate evolutionary computation, which is a nature-inspired algorithm in the context of dynamic social network clustering. Several social
networks, with recorded time stamps, will be used to depict a growing social network. The
optimal clusters are determined with each given time stamp. Each time stamp will provide a
fairly different cluster structure and a possibly different cluster count.
Subject
DRNTU::Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Type
Final Year Project (FYP)
Rights
Nanyang Technological University
Collections
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