Graph clustering is a basic technique in machine learning, and has widespread applications
in different domains. While spectral techniques have been successfully applied for clustering …

Networks often possess mesoscale structures, and studying them can yield insights into both
structure and function. It is most common to study community structure, but numerous other …

Applied network science often involves preprocessing network data before applying a
network-analysis method, and there is typically a theoretical disconnect between these …

The study of network structure is pervasive in sociology, biology, computer science, and
many other disciplines. One of the most important areas of network science is the algorithmic …

This paper is motivated by the task of detecting anomalies in networks of financial
transactions, with accounts as nodes and a directed weighted edge between two nodes …

Finding group elements from noisy measurements of their pairwise ratios is also known as
the group synchronization problem, first introduced in the context of the group SO (2) of …

Complex networks with directed, local interactions are ubiquitous in nature and often occur
with probabilistic connections due to both intrinsic stochasticity and disordered …

We introduce a principled method for the signed clustering problem, where the goal is to
partition a graph whose edge weights take both positive and negative values, such that …

We study community structure in time-dependent legislation cosponsorship networks in the
Peruvian Congress, and we compare them briefly to legislation cosponsorship networks in …

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 …