Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Graph representations offer powerful and intuitive ways to describe data in a multitude of
application domains. Here, we consider stochastic processes generating graphs and …

Change point detection is crucial for identifying state transitions and anomalies in dynamic
systems, with applications in network security, health care, and social network analysis …

Abstract We present Graph Random Neural Features (GRNF), a novel embedding method
from graph-structured data to real vectors based on a family of graph neural networks. The …

We present an anomaly and outlier detection method for graph data. The method relies on
the consideration that anomalies and outliers are more easily isolated by certain incremental …

Given a finite sequence of graphs, eg coming from technological, biological, and social
networks, the paper proposes a methodology to identify possible changes in stationarity in …

The space of graphs is characterized by a non-trivial geometry, which often complicates
performing inference in practical applications. A common approach is to use embedding …

Map** complex input data into suitable lower dimensional manifolds is a common
procedure in machine learning. This step is beneficial mainly for two reasons:(1) it reduces …

This thesis explores the field of graph neural networks, a class of deep learning models
designed to learn representations of graphs. We organise the work into two parts. In the first …

Abstract We present Graph Random Neural Features (GRNF), a novel embedding method
from graphstructured data to real vectors based on a family of graph neural networks. The …