ParSocial 2016 - IEEE Workshop on Parallel and Distributed Processing for Computational Social Systems (ParSocial 2016)

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
Social network based models
Models of social interactions (e.g. influence spread, group formation, group stability, and social resilience)
Complex Adaptive System (CAS) models (e.g. modeling emergence in social systems)
Models incorporating socio-cultural factors
Novel agent based social modeling and simulation
Modeling with uncertain, incomplete social data
Models using real-time social data
Representations of social and behavioral theories in computational models
Simulation methodologies for social processes including numerical and statistical methods
Models for network dynamism
Modeling human and social elements in cyber systems (e.g. cyber-physical systems, socio-technical systems, and network centric systems)
Social Computing Algorithms for Parallel and Distributed Platforms
Analysis of massive social data
Algorithms for dynamic social data
Algorithms for social network analysis
Analysis methods for incomplete, uncertain social data
Social analysis methods on parallel and distributed frameworks
Social computing for emerging architectures (e.g. cloud, multi-core/many-core, GPU, and mobile computing architectures)
Application
Emergency management (e.g. infrastructure resilience, natural disaster management)
National security (e.g. political stability, counter-terrorism, and homeland security)
Health science (e.g. disease spread models, health informatics, and health care analytics)
Social media analytics (e.g. business analytics, political analysis, and economic analysis)