High-order multi-view clustering for generic data
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 …
approaches. However, in many real-world scenarios, the graph structure of data is not given …
A tighter analysis of spectral clustering, and beyond
P Macgregor, H Sun - International Conference on Machine …, 2022 - proceedings.mlr.press
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 …
some graph G=(V_G, E_G) into R^ k using k eigenvectors of some matrix of G, and applies k …
[HTML][HTML] Lead–lag detection and network clustering for multivariate time series with an application to the US equity market
S Bennett, M Cucuringu, G Reinert - Machine Learning, 2022 - Springer
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 …
partially lead the evolution of the system, while other variables follow this evolution with a …
Cheeger inequalities for directed graphs and hypergraphs using reweighted eigenvalues
We derive Cheeger inequalities for directed graphs and hypergraphs using the reweighted
eigenvalue approach that was recently developed for vertex expansion in undirected …
eigenvalue approach that was recently developed for vertex expansion in undirected …
Harmonic analysis on directed graphs and applications: From Fourier analysis to wavelets
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 …
connected directed graph (digraph) of which the random walk operator is the cornerstone …
[HTML][HTML] Chained structure of directed graphs with applications to social and transportation networks
A Concas, C Fenu, L Reichel, G Rodriguez… - Applied Network …, 2022 - Springer
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 …
studies directed graphs and defines the notions of ℓ-chained and {ℓ, k}-chained directed …
[HTML][HTML] Estimating mixed memberships in directed networks by spectral clustering
H Qing - Entropy, 2023 - mdpi.com
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 …
complex networks in social network analysis. This paper considers the problem of estimating …
Higher-Order Functional Structure Exploration in Heterogeneous Combat Network Based on Operational Motif Spectral Clustering
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 …
the interactions among multiple individuals in complex systems. With the development of …
DIGRAC: digraph clustering based on flow imbalance
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 …
network framework, named DIGRAC, to obtain node embeddings for directed networks in a …
Local algorithms for finding densely connected clusters
P Macgregor, H Sun - International Conference on Machine …, 2021 - proceedings.mlr.press
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 …
and has been widely applied in many research fields of data science. While the objective of …