[DBWorld] Call For Papers, CSoNet 2021
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Call For Papers
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CSoNet 2021 provides a premier interdisciplinary forum to bring together re=
searchers and practitioners from all fields of big data and social networks=
, such as billion-scale network computing, social network/media analysis, m=
ining, security and privacy, and deep learning and applications. CSoNet 202=
1 seeks to address emerging yet important computational problems, with a fo=
cus on the fundamental background, theoretical developments, and real-world=
applications associated with big data network analysis, modelling, and dee=
p learning and understanding. The conference solicits theoretical, methodol=
ogical, empirical, and experimental research reporting original and unpubli=
shed results on computational big data and social networks. Topics of inter=
est include, but are not limited to:
Real-world Complex Networks Analysis
Trends and Pattern Analysis in Social Networks
Representation Learning on Networks
Big Data Analysis
Mathematical Modeling and Analysis of Real-world Social Platforms
Network Structure Analysis and Dynamics Optimization
Data Network Design and Architecture
Information Diffusion Models and Techniques
Security and Privacy in Data Networks and Analysis
Efficient Algorithms for Large-scale Data Networks Computing
Reputation and Trust in Social Media
Social Influence, Recommendation, and Media
Applications of Complex Data Network Analysis
Energy Efficiency in Mobile Data Networks
Natural Language Understanding and Applications for Social Media
E-commerce and Social Media Marketing
Deep Learning on Graphs and its Applications
Stock Market Prediction and Stock Recommendation with Social Media Data
Anomaly Detection, Security, and Privacy in Big Data Networks
Analysis of Signed and Attributed Real-world Networks
Multidimensional Graph Analysis
Algorithmic Fairness in Social Network Analysis and Graph Mining.
Socially-relevant Analytics from Social Media Contents (e.g., Bias, Toxicit=
y, etc.)
Accepted papers will be published in Springer=E2=80=99s Lecture Notes in Co=
mputer Science, and indexed by ISI (CPCI-S, included in ISI Web of Science)=
, EI Engineering Index (Compendex and Inspec databases), ACM Digital Librar=
y, DBLP, Google Scholar, MathSciNet, etc. Also, extended versions of select=
ed best papers will be invited for publication in the Journal of Combinator=
ial Optimization, IEEE Transactions on Network Science and Engineering, and=
Computational Social Networks.
Authors who are interested in the above topics can submit their unpublished=
work to CSoNet 2021. A clear indication of the motivation and comparison w=
ith prior related work should be presented. Simultaneous submission to a jo=
urnal or another conference with refereed proceedings is not allowed.
Submissions must adhere to the following guidelines:
Papers must be formatted using the LNCS format (ftp://ftp.springernature.co=
m/cs-proceeding/llncs/llncs2e.zip) without altering margins or the font poi=
nt.
The maximum length of a regular paper (including references) is 12 pages; 2=
pages for an extended abstract.
Proofs omitted due to space constraints must be placed in an appendix to be=
read by the program committee members at their discretion.
Submission link: https://easychair.org/conferences/?conf=3Dcsonet2021
Organizing Committee =
General Chairs
Long Le, University of Quebec
Jun Pei, Hefei University of Technology
Program Chairs
Ruoming Jin, Kent State University
David Mohaisen, University of Central Florida
Important Dates:
Paper Submission June 20, 2021
Acceptance Notification September 10, 2021
Camera Ready & Registration September 24, 2021
Conference Dates November 15-17, 2021
Conference Mode of Operation:
The conference is planned as an in-person conference, allowing those who ca=
nnot be present in person to attend and present virtually. Depending on the=
development of COVID-19 and the vaccination, the mode of the conference wi=
ll be revised accordingly. =
More Information about the conference and the organizers is available at ht=
tp://optnetsci.cise.ufl.edu/CSoNet/ =
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