Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

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 …

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …

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 …

Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

Many networks can be usefully decomposed into a dense core plus an outlying, loosely
connected periphery. Here we propose an algorithm for performing such a decomposition …

For many networks of scientific interest we know both the connections of the network and
information about the network nodes, such as the age or gender of individuals in a social …

One of the main computational and scientific challenges in the modern age is to extract
useful information from unstructured texts. Topic models are one popular machine-learning …

Discovering and characterizing the large-scale topological features in empirical networks
are crucial steps in understanding how complex systems function. However, most existing …

We consider three distinct and well-studied problems concerning network structure:
community detection by modularity maximization, community detection by statistical …