Graph-based multi-view clustering has achieved better performance than most non-graph
approaches. However, in many real-world scenarios, the graph structure of data is not given …

This work studies the classical spectral clustering algorithm which embeds the vertices of
some graph G=(V_G, E_G) into R^ k using k eigenvectors of some matrix of G, and applies k …

In multivariate time series systems, it has been observed that certain groups of variables
partially lead the evolution of the system, while other variables follow this evolution with a …

We derive Cheeger inequalities for directed graphs and hypergraphs using the reweighted
eigenvalue approach that was recently developed for vertex expansion in undirected …

We introduce a novel harmonic analysis for functions defined on the vertices of a strongly
connected directed graph (digraph) of which the random walk operator is the cornerstone …

The need to determine the structure of a graph arises in many applications. This paper
studies directed graphs and defines the notions of ℓ-chained and {ℓ, k}-chained directed …

Community detection is an important and powerful way to understand the latent structure of
complex networks in social network analysis. This paper considers the problem of estimating …

Higher-order organization of complex networks is an effective way to understand and model
the interactions among multiple individuals in complex systems. With the development of …

Node clustering is a powerful tool in the analysis of networks. We introduce a graph neural
network framework, named DIGRAC, to obtain node embeddings for directed networks in a …

Local graph clustering is an important algorithmic technique for analysing massive graphs,
and has been widely applied in many research fields of data science. While the objective of …