[Om-announce] CFP: Special Issue on Social Networking Big Data, FGCS

[Shui Yu]

*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) <psc346 at aliyun.com>

Shui Yu <syu at deakin.edu.au>

Peter Mueller <pmu at 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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