الفهرس 
Title : Knowledge sharing over social network
AbstractOnly 14 pages are availabe for public view
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Abstract
In recent years, the amount of data shared through social network among users with different cultures is increasing over days; this phenomenon leads to information overloading, which constitutes a dramatic problem for users in business and social communities. Personalization plays an important role in helping users to select useful contents to avoid wasting their time and effort. Personalization also supports knowledge sharing in the social network in many aspects, such as: {u25AA} Expert identification, {u25AA} Information propagation, and {u25AA} Community detection which was chosen to be the main objective of this research. With the evolution of social network, users tend to belong to different communities. A community in social network is a group of different types of users who share the same interests and interact with each other through the network. Discovering hidden communities is considered one of the valuable research area in social network analysis since it allows the extraction of useful knowledge from this rich pool of information. The process of discovering communities in the social network helps create new connections between users in the same community and encourages them to be more active in the network. Furthermore, the growth of the dynamic behavior of the users in the network is a good indicator of the network status and its health. Accordingly, the capability to extract hidden communities based on user interests is becoming vital for a wide variety of applications, such as product recommendation, marketing, elections, stock index and computer science