الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
The field of social network analysis has witnessed an unprecedented
growth in its applications during the past years. Social network analysis is used
to extract patterns of relationships between social actors in order to discover
the underlying social stru
cture. One of the most important problems in social
network analysis is Community detection. Many algorithms have been
proposed to detect communities in static networks but these methods require
more improvement to enhance community detection. Furthermore,
most studies
have focused on the detection of communities in static networks, where static
networks do not simulate reality and cannot be reliably applied to study
dynamic processes. Moreover, online social networks are always evolving in
nature: pages ap
pear or are updated constantly; people start new relationships,
etc. Thus, recent researches have been conducted to investigate the case of
communities in dynamic networks, which is at the heart of this thesis.
This thesis firstly enhanced existing comm
unity detection algorithms in
static networks. Secondly, proposed computational solutions for the problem of
detecting communities especially in social networks which change over time.
This thesis consists of five chapters organized as follows:
Chapter On
e:
Presents a introduction for social networks, social network
analysis and basics of graph theory, objective and thesis organization.
Chapter Two:
Titled under ”Related Work” this chapter discusses the
definition of community structure and its applica
tions, methods for community
detection on static and dynamic networks, how to evaluating communities, and
the datasets applied in this work.
Chapter Three
: Titled under ”A new pre
-
processing strategy for improving
community detection algorithms” this c
hapter presents the proposed new pre
-
processing steps for enhancing existing community detection algorithms. Also,
presents the experimental results and the discussions about these results.
Chapter Four:
Titled under ”An efficient and fast algorithm for
detecting
community structure in dynamic social network” this chapter introduces our
proposed algorithm for detecting community structure in the dynamic social
network. Also, presents the experimental results and the discussions about
these resul