الفهرس 
Background
Social Network RepresentationsOnly 14 pages are availabe for public view
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Abstract
Online social networks emergence enables individuals to communicate and share
their opinions and feedback. Social networks mining aims at applying mining
techniques to online social networks to reveal interesting hidden patterns on human
behavior and interaction. Organizations utilize social network mining to expand
their market and to improve customer social relations.
In this work, we propose a novel social network mining approach for social
customer relationship analysis. In our approach, we propose a community
detection technique which benefits from the most influential users on the network.
As communities tend to be formed around users of great influence on their peers,
the proposed approach utilizes such influential users to build communities around
them.
Moreover, we propose a new community detection algorithm that incorporates
behavioral information attached to users in the social network. Using such
behavioral data of nodes is for the aim of detecting communities that are closely
mapped to the underlying behavioral communities in real social networks.
We use the behavioral data, namely, the actions done by users on their social
network to propose a new similarity measure to measure the degree of similarity
between users. Furthermore, the proposed algorithm uses the demographic data of
users to enhance the quality of communities detected.
Experimental evaluation on two real social network datasets has been carried out
and the results show that the proposed social network mining approach surpasses
others in respect of all the evaluation measures used which indicates the ability of
the proposed approach in identifying communities with high quality.