The prevalence of graph-based data has spurred the rapid development of graph neural
networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets …

Networks are ubiquitous in many real-world applications (eg, social networks encoding
trust/distrust relationships, correlation networks arising from time series data). While many …

Recovering global rankings from pairwise comparisons has wide applications from time
synchronization to sports team ranking. Pairwise comparisons corresponding to matches in …

Complex numbers define the relationship between entities in many situations. A canonical
example would be the off-diagonal terms in a Hamiltonian matrix in quantum physics …

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 …

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 …

Signed directed graphs contain both sign and direction information on their edges, providing
richer information about real-world phenomena compared to unsigned or undirected graphs …

Abstract Graph Contrastive Learning (GCL) is an effective method for discovering
meaningful patterns in graph data. By evaluating diverse augmentations of the graph, GCL …

The time proximity of trades across stocks reveals interesting topological structures of the
equity market in the United States. In this article, we investigate how such concurrent cross …

Clustering is an important topic in algorithms, and has a number of applications in machine
learning, computer vision, statistics, and several other research disciplines. Traditional …