Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.compeleceng.2013.10.001 | Computers & Electrical Engineering |
Keywords | Field | DocType |
social network,social networking site,collective behavior,spectral clustering,interaction information,social dimensionality,collective behavior learning system,sparse information,sndr service,social network dimensionality refinement,feature selection | Data science,Kernel (linear algebra),Middleware,Collective behavior,Spectral clustering,Social network,Feature selection,Computer science,Curse of dimensionality,Artificial intelligence,Interaction information,Machine learning | Journal |
Volume | Issue | ISSN |
39 | 8 | 0045-7906 |
Citations | PageRank | References |
2 | 0.36 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Xie | 1 | 47 | 14.48 |
| Bin Luo | 2 | 802 | 107.57 |
| Rongbin Xu | 3 | 37 | 10.01 |