Abstract: Spectral graph theory is the study of the eigenvalues and eigenvectors of matrices associated with graphs. In this tutorial, we will try to provide some intuition as to why these ...

Spectral clustering is an unsupervised learning technique that identifies clusters in data by analyzing the eigenstructure of a similarity matrix. Unlike traditional clustering algorithms that use ...

The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning. To understand this inadequacy, there is a need to ...

We consider the random reversible Markov kernel K obtained by assigning i.i.d. nonnegative weights to the edges of the complete graph over n vertices and normalizing by the corresponding row sum. The ...

In this paper, we study the spectral radius of bipartite graphs. Let 𝐺 be a bipartite graph with 𝑒 edges without isolated vertices. It was known that the spectral radius of 𝐺 is at most the square ...

@article{jin2025expressive, title={Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting}, author={Ming Jin and Guangsi Shi and Yuan-Fang Li and Bo Xiong and Tian Zhou ...