Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Dynamic social network analysis using latent space models
ACM SIGKDD Explorations Newsletter
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
A tutorial on spectral clustering
Statistics and Computing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Homophily in the Digital World: A LiveJournal Case Study
IEEE Internet Computing
Toward Predicting Collective Behavior via Social Dimension Extraction
IEEE Intelligent Systems
Social networking federation: A position paper
Computers and Electrical Engineering
Scalable Learning of Collective Behavior
IEEE Transactions on Knowledge and Data Engineering
Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation
Computers and Electrical Engineering
CP2: Cryptographic privacy protection framework for online social networks
Computers and Electrical Engineering
| Hi-index | 0.00 |
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.