Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

Graph databases (GDBs) are crucial in academic and industry applications. The key
challenges in developing GDBs are achieving high performance, scalability …

Despite the increasing interest in graph databases their requirements and specifications are
not yet fully understood by everyone, leading to a great deal of variation in the supported …

Important graph mining problems such as Clustering are computationally demanding. To
significantly accelerate these problems, we propose ProbGraph: a graph representation that …

Graph databases (GDBs) are crucial in academic and industry applications. The key
challenges in developing GDBs are achieving high performance, scalability …

This thesis contributes to the field of network and graph data analysis. Over the course of
three chapters, we propose novel statistical methods that address the challenges of …

Nowadays, we are producing and collecting data at an unprecedented rate, measured in the
order of petabytes per minute, together with a substantial increase in data volume …

This work is done as part of a research master's thesis project. The goal is to generate
SPARQL queries based on user-supplied keywords to query RDF graphs. To do this, we first …