A scalable matrix-based framework for social dynamics learning
The objective of this project is to design a scalable framework for dynamic learning in social networks. The framework will enable robust learning which is resilient to network evolution and provides integrity. Social dynamics will be investigated. Matrix factorization methods will be used to build computational heterogeneous models to discover patterns in real time. Reliability and robustness of the framework will be implemented by integrating heterogeneous data types and filtering out fraudulent information.
Use of FutureSystems
FutureGrid will be used to compare performance of algorithms on GPU using CUDA technology and Matlab.
Scale of Use
run experiments approximately a few times per month.