*Special Issue on Social Networking Big Data Opportunities, Solutions, and Challenges*
http://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-social-networking-big-data-opportunities-so/ Social Networking Big Data is a collection of very huge data sets with a great diversity of types from social networks. The emerging paradigm of social networking and big data provides enormous novel approaches for efficiently adopting advanced networking communications and big data analytic schemas by using the existing mechanism. The rapid development of Social Networking Big Data brings revolutionary changes to our daily lives and global business, which has been addressed by recent research. However, as attackers are taking advantages of social networks to achieve their malicious goals, the security issue is also a critical concern when using Social Networking Big Data in practice. Due to the complexity and diversity of the Social Networking Big Data, there are two important aspects of Social Networking Big Data. One is how to conduct social network analysis based on Big Data, the other is how to using Big Data analytic technique to ensure security of social networks using various security mechanism. Current work on Social Networking Big Data focuses on information processing, such as data mining and analysis. However, security, trust and privacy of Social Networking Big Data are remarkably significant for current researchers and practitioners to address these issues, and seek out the efficient methods to different threats. This special issue will concentrates on the challenging topic – “Social Networking Big Data”, and aims to solicit both original research and tutorial papers that discuss the security, trust and privacy of Social Networking Big Data. Any topic related to Social Networking Big Data aspects, including social networks, social influence analysis, big data, security, trust and privacy, will be considered. All aspects of design, theory and realization are of interest. The scope and interests for the special issue include but are not limited to the following list: *(i) Fundamentals and Technologies for Social Networking Big Data* Modeling on social influence with big data Social influence analysis with big data Influence propagation in large‐scale social networks Dynamic social influence in large‐scale social networks Influence maximization problem with big data User behavior analysis with social influence evaluation Social influence analysis in heterogeneous social network Casual relationship in large‐scale social networks Methods for distinguishing the positive, negative, and controversy influence Models, methods, and tools for influence propagation Community detection methods with big data Modeling community influence in social networks Impact of social networks on human social behavior Human behavior analysis in social networks with big data Impact of social networks on human social behavior Recommendations and advertising in social networks with big data Modeling on the characteristics and mechanisms of social networks *(ii) Security, Trust and Privacy for Social Networking Big Data* Modeling on malicious information propagation with social influence analysis Secure social networking application with social influence analysis Privacy in management and analysis of social networking big data Prevention of malware propagation in social networks Modeling on the secure mechanisms of social networks Novel secure solutions for designing, supporting and operating social networks Trust evaluation in social networks with big data Threat and vulnerability analysis in social networks Secure social network architecture with big data Privacy protection in social networks with big data Secure social networking applications with big data Security design for social networks in big data Models, methods, and tools for testing the security of social networks Trust management in social networks with big data Spam problems in social networks with big data Detection for malicious information propagation in social networks Submission Format and Guideline All submitted papers must be clearly written in excellent English and contain only original work, which has not been published by or is currently under review for any other journal or conference. Papers must not exceed 25 pages (one‐column, at least 11pt fonts) including figures, tables, and references. A detailed submission guideline is available as “Guide to Authors” at: https://www.elsevier.com/journals/future-generation-computer-systems/0167-739X/guide-for-authors All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select “SI: SNBD-OSC” when they reach the “Article Type” step in the submission process. The EES website is located at: http://ees.elsevier.com/fgcs All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the guest editors. *Important dates * Submission deadline: December 15, 2016 First-round pass notification: (for a rejected paper) December 30, 2016 Review result notification: April 1, 2017 Acceptance/rejection notification: June 1, 2017 Publication: December, 2017 *Guest Editors:* Sancheng Peng (corresponding guest editor) <psc...@aliyun.com> Shui Yu <s...@deakin.edu.au> Peter Mueller <p...@zurich.ibm.com> -- ----------------------------- Shui YU, PhD, Senior Lecturer School of Information Technology, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia. Telephone:0061 3 9251 7744 http://www.deakin.edu.au/~syu
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