Call for Papers
Computational
methods to represent, model and analyze problems using social
information have come a long way in the last decade. Computational
methods, such as social network analysis, have provided exciting
insights into how social information can be utilized to better
understand social processes, and model the evolution of social systems
over time. We have also seen a rapid proliferation of sensor
technologies, such as smartphones and medical sensors, for collecting a
wide variety of social data, much of it in real time. Meanwhile, the
emergence of parallel architectures, in the form of
multi-core/many-core processors, and distributed platforms, such as
MapReduce, have provided new approaches for large-scale modeling and
simulation, and new tools for analysis. These two trends have
dramatically broadened the scope of computational social systems
research, and are enabling researchers to tackle new challenges. These
challenges include modeling of real world scenarios with dynamic and
real-time data, and formulating rigorous computational frameworks to
embed social and behavioral theories. This workshop provides a platform
to bring together interdisciplinary researchers from areas, such as
computer science, social sciences, applied mathematics and computer
engineering, to showcase innovative research in computational social
systems that leverage the emerging trends in parallel and distributed
processing, computational modeling, and high performance computing.
Areas of research
interests and domains of applications include,
but are not limited to:
Large-Scale Modeling and Simulation for Social Systems
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Social Computing Algorithms for Parallel and Distributed Platforms
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Application
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For ParSocial 2017, selected papers were invited to submit extended versions to a special issue of the IEEE Transactions on Computational Social Systems. For Par Social 2018, there are also plans to invite extended versions of selected papers to a special journal issue.