Maher, Rana
(2017)
Online Social Networks:
Measurements, Analysis and Solutions for
Mining Challenges.
Masters thesis, National University of Ireland Maynooth.
Abstract
In the last decade, online social networks showed enormous growth. With the rise
of these networks and the consequent availability of wealth social network data, Social
Network Analysis (SNA) led researchers to get the opportunity to access, analyse and
mine the social behaviour of millions of people, explore the way they communicate and
exchange information.
Despite the growing interest in analysing social networks, there are some challenges
and implications accompanying the analysis and mining of these networks. For example,
dealing with large-scale and evolving networks is not yet an easy task and still requires
a new mining solution. In addition, finding communities within these networks is a
challenging task and could open opportunities to see how people behave in groups on a
large scale. Also, the challenge of validating and optimizing communities without knowing
in advance the structure of the network due to the lack of ground truth is yet another
challenging barrier for validating the meaningfulness of the resulting communities.
In this thesis, we started by providing an overview of the necessary background and key
concepts required in the area of social networks analysis. Our main focus is to provide
solutions to tackle the key challenges in this area. For doing so, first, we introduce a predictive
technique to help in the prediction of the execution time of the analysis tasks for
evolving networks through employing predictive modeling techniques to the problem of
evolving and large-scale networks. Second, we study the performance of existing community
detection approaches to derive high quality community structure using a real email
network through analysing the exchange of emails and exploring community dynamics.
The aim is to study the community behavioral patterns and evaluate their quality within
an actual network. Finally, we propose an ensemble technique for deriving communities
using a rich internal enterprise real network in IBM that reflects real collaborations
and communications between employees. The technique aims to improve the community
detection process through the fusion of different algorithms.
Item Type: |
Thesis
(Masters)
|
Keywords: |
Online Social Networks; Measurements; Analysis; Solutions;
Mining Challenges; |
Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: |
9904 |
Depositing User: |
IR eTheses
|
Date Deposited: |
11 Sep 2018 14:14 |
URI: |
|
Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
Repository Staff Only(login required)
|
Item control page |
Downloads per month over past year
Origin of downloads