The learning system of collective behavior in students' social network

摘要

The rapid development of social networking sites brings about many data mining tasks and novel challenges. We focus on classification tasks with students' interaction information in a social network. To mitigate the difficulties of developing a learning system, this study proposes a new computing paradigm: spectral clustering as a service, providing a service to enable exacting social dimensionality on demand. Spectral clustering has been developed in a social network dimensionality refinement model as a kernel middleware, namely SNDR. The SNDR service can process the sparse information, explore the network's topology and finally exact suitable features. Experimental results justify the design of Collective Behavior Learning System and the implementation of the Social Network Dimensionality Refinement model's service. Our system makes better performance than baseline methods.