Graph evolution via social diffusion processes

Authors:
Dijun Luo;Chris Ding;Heng Huang
Affiliations:
Department of Computer Science and Engineering, University of Texas, Arlington, Texas;Department of Computer Science and Engineering, University of Texas, Arlington, Texas;Department of Computer Science and Engineering, University of Texas, Arlington, Texas
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Year:
2011

Citing 7
Cited 0

Spectral K-way ratio-cut partitioning and clustering

DAC '93 Proceedings of the 30th international Design Automation Conference
An Introduction to Variational Methods for Graphical Models

Machine Learning
Generalized clustering, supervised learning, and data assignment

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures

Bioinformatics
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies

Journal of the ACM (JACM)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new stochastic process, called as Social Diffusion Process (SDP), to address the graph modeling. Based on this model, we derive a graph evolution algorithm and a series of graphbased approaches to solve machine learning problems, including clustering and semi-supervised learning. SDP can be viewed as a special case of Matthew effect, which is a general phenomenon in nature and societies. We use social event as a metaphor of the intrinsic stochastic process for broad range of data. We evaluate our approaches in a large number of frequently used datasets and compare our approaches to other state-of-the-art techniques. Results show that our algorithm outperforms the existing methods in most cases. We also applying our algorithm into the functionality analysis of microRNA and discover biologically interesting cliques. Due to the broad availability of graph-based data, our new model and algorithm potentially have applications in wide range.