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Social network analysis and its application
Author(s) -
Qiang Weizhong,
Zheng Xianghan,
Hsu ChingHsien
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3920
Subject(s) - social network analysis , social network (sociolinguistics) , computer science , sociology , world wide web , social media
The purpose of this special issue is to collate a selection of representative research articles that were primarily presented at the 2015 International Conference on Cloud Computing and Big Data [1]. This conference brings together researchers and industry practitioners in order to exchange information regarding advancements in the state-of-the-art and practice of cloud computing, big data, and social network, as well as to identify emerging research topics and define the future directions of cloud computing, big data, and social network. Nowadays, various social applications such as blogs, e-mail, instant messaging, social networking (Facebook, Twitter, LinkedIn, etc.), wikis, and social bookmarking have been widely popularized by providing digital platforms for social interaction. Today’s online social network or mobile social network pervades all aspects of our daily lives and contains vast amount of data. From this vast amount of data, ability is needed to extract and analyze the social networks of a new era that can be consisted of millions of nodes and connections. Meanwhile, various critical issues such as clustering and evolution mining of social networks, modeling and understanding of social behaviors via computational means, information spread and modeling, social influence analysis, social recommendations, etc., provide significant challenges. This special issue is devoted to analysis of these large-scale social structures and what is more important to identify the areas where social network analysis can be applied and provide the knowledge that is not accessible for other types of analysis.