Current modularity-based community detection algorithms attempt to find cluster
memberships that maximize modularity within a fixed graph topology. Diverging from this …

Graph representation learning and its applications have gained significant attention in
recent years. Notably, Graph Neural Networks (GNNs) and Graph Signal Processing (GSP) …

High-throughput single-cell immune profiling technologies, such as mass cytometry (CyTOF)
and single-cell RNA sequencing measure the expression of multiple proteins or genes …

This thesis focuses on graph learning for semi-supervised learning tasks to mitigate the
impact of noise in real-world graphs. One approach to learn graphs is using bilevel …

Computational Bayesian inference has numerous applications in many branches of signal
processing and machine learning. Bayesian techniques allow for principled modeling of …

Estimating the graphical structures of high dimensional data and identifying the presence of
clusters in them are ubiquitous tasks in every scientific domain that deals with interacting or …

Gradient scarcity emerges when learning graphs by minimizing a loss on a subset of nodes
under the semi-supervised setting. It consists in edges between unlabeled nodes that are far …

Community detection and graph learning are two important problems in graph analysis. The
former problem deals with topological analysis of graphs to identify their mesoscale …

Graphs offer a simple yet meaningful representation of relationships between data. This
representation is often used in machine learning algorithms in order to incorporate structural …

Graph representation learning and its applications have gained significant attention in
recent years. Notably, Graph Neural Networks (GNNs) and Graph Signal Processing (GSP) …