l1-Graph based community detection in online social networks

Authors:
Liang Huang;Ruixuan Li;Yuhua Li;Xiwu Gu;Kunmei Wen;Zhiyong Xu
Affiliations:
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China;Department of Math and Computer Science, Suffolk University, Boston
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

Citing 3
Cited 0

Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

The Journal of Machine Learning Research
Learning with l1-graph for image analysis

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detecting community structures in online social network is a challenging job for traditional algorithms, such as spectral clustering algorithms, due to the unprecedented large scale of the network. In this paper, we present an efficient algorithm for community detection in online social network, which chooses relatively small sample matrix to alleviate the computational cost. We use ℓ1-graph to construct the similarity graph and integrate the graph laplacian with random walk in directed social network. The experimental results show the effectiveness of the proposed method.