The learning system of collective behavior in students’ social network

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.

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• We address the learning system due to thousands of students’ collective behavior.
• We design a social network dimensionality refinement(SNDR) to exact the feature in network.
• We explore the feasibility of using SNDR model to deploy the discriminative learning.
• Increasing the Macro and Micro Evaluation information with considering the sensitivity of dimensions and labeled nodes.