An improved spectral graph partitioning algorithm for mapping parallel computations
SIAM Journal on Scientific Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiway partitioning via geometric embeddings, orderings, and dynamic programming
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Spectral graph partitioning is a powerful tool for unsupervised data learning. Most existing algorithms for spectral graph partitioning directly utilize the pairwise similarity matrix of the data to perform graph partitioning. Consequently, they are incapable of fully capturing the intrinsic structural information of graphs. To address this problem, we propose a novel random walk diffusion similarity measure (RWDSM) for capturing the intrinsic structural information of graphs. The RWDSM is composed of three key components—emission, absorbing, and transmission. It is proven that graph partitioning on the RWDSM matrix performs better than on the pairwise similarity matrix of the data. Moreover, a spectral graph partitioning objective function (referred to as DGPC) is used for capturing the discriminant information of graphs. The DGPC is designed to effectively characterize the intra-class compactness and the inter-class separability. Based on the RWDSM and DGPC, we further develop a novel spectral graph partitioning algorithm (referred to as DGPCA). Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the developed DGPCA.