Spectral graph theory

About: Spectral graph theory is a research topic. Over the lifetime, 1334 publications have been published within this topic receiving 77373 citations.

A Semi-Supervised Clustering Method Based on Graph Contraction and Spectral Graph Theory

Abstract: Semi-supervised learning is a machine learning framework where learning from data is conducted by utilizing a small amount of labeled data as well as a large amount of unlabeled data (Chapelle et al., 2006). It has been intensively studied in data mining and machine learning communities recently. One of the reasons is that, it can alleviate the time-consuming effort to collect “ground truth” labeled data while sustaining relatively high performance by exploiting a large amount of unlabeled data. (Blum & Mitchell, 1998) showed the PAC learnability of semi-supervised learning, especially in classification problem.

The Quasi-Laplacian Spectral Radius of a Graph

Abstract: An inequality about degree of vertex and quasi-Lap|acian spectral radius of a simple graph can be given,and a new upper bound of the quasi-Laplacian spectral radius of a graph is obtained.

Spectral Graph Drawing: Building Blocks and Performance Analysis

Abstract: The objective of a graph drawing algorithm is to automatically produce an aesthetically-pleasing two or three dimensional visualization of a graph. Spectral graph theory refers to the study of eigenvectors of matrices derived from graphs. There are several well-known graph drawing algorithms that use insights from spectral graph theory. In this work, we experiment with two spectral methods to generate drawings of large-scale graphs. We also combine spectral drawings with the multilevel approach, and this leads to a larger collection of implementations. We analyze drawing quality and performance tradeoffs with these approaches.