The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …

Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

Community detection is often used to understand the structure of large and complex
networks. One of the most popular algorithms for uncovering community structure is the so …

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical
and practical perspectives. They are one of the most fundamental types of stochastic …

The network architecture of the human brain has become a feature of increasing interest to
the neuroscientific community, largely because of its potential to illuminate human cognition …

The development of new technologies for mapping structural and functional brain
connectivity has led to the creation of comprehensive network maps of neuronal circuits and …

The brain is an inherently dynamic system, and executive cognition requires dynamically
reconfiguring, highly evolving networks of brain regions that interact in complex and …

Machine unlearning is a process of removing the impact of some training data from the
machine learning (ML) models upon receiving removal requests. While straightforward and …

Across many scientific domains, there is a common need to automatically extract a simplified
view or coarse-graining of how a complex system's components interact. This general task is …