Comparing graphs by means of optimal transport has recently gained
signi...
Obtaining sparse, interpretable representations of observable data is cr...
We present Topological Point Cloud Clustering (TPCC), a new method to cl...
We present PyGenStability, a general-use Python software package that
pr...
We study the task of node classification for graph neural networks (GNNs...
We study linear filters for processing signals supported on abstract
top...
We propose a method to detect outliers in empirically observed trajector...
The processing of signals supported on non-Euclidean domains has attract...
Recent studies have exposed that many graph neural networks (GNNs) are
s...
Higher-order networks have so far been considered primarily in the conte...
This paper re-examines the concept of node equivalences like structural
...
Modeling complex systems and data using the language of graphs and netwo...
In this paper, we study linear filters to process signals defined on
sim...
This tutorial paper presents a didactic treatment of the emerging topic ...
Networks are a widely-used tool to investigate the large-scale connectiv...
We study the problem of recovering a planted hierarchy of partitions in ...
Modular and hierarchical structures are pervasive in real-world complex
...
We consider a blind identification problem in which we aim to recover a
...
We present a graph-based semi-supervised learning (SSL) method for learn...
We discuss a variant of `blind' community detection, in which we aim to
...
This paper focuses on devising graph signal processing tools for the
tre...
Modeling complex systems and data with graphs has been a mainstay of the...
This chapter discusses the interplay between structure and dynamics in
c...
Networks provide a powerful formalism for modeling complex systems, by
r...
Graphs provide a natural mathematical abstraction for systems with pairw...