Networks often exhibit structure at disparate scales. We propose a method for identifying
community structure at different scales based on multiresolution modularity and consensus …

Spectral clustering has become a popular technique due to its high performance in many
contexts. It comprises three main steps: create a similarity graph between N objects to …

Uncovering modular structure in networks is fundamental for systems in biology, physics,
and engineering. Community detection identifies candidate modules as hypotheses, which …

Complex networks can often be divided in dense sub-networks called communities. These
communities are crucial in understanding the underlying structure of these networks and …

Background Several standalone error correction tools have been proposed to correct
sequencing errors in Illumina data in order to facilitate de novo genome assembly. However …

The community detection in complex networks has become a major field of research.
Disjoint community detection deals often with getting a partition of nodes where every node …

The ever increasing adoption of mobile technologies and ubiquitous services allows to
sense human behavior at unprecedented levels of details and scale. Wearable sensors are …

Identifying community structure is a fundamental problem in network analysis. Most
community detection algorithms are based on optimizing a combinatorial parameter, for …

Die Arbeit wurde im Frühjahrstrimester 2018 von der Bucerius Law School als Dissertation
angenommen; alle Auswertungen sind auf dem Stand von Januar 2018. Ich danke meinem …

We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the
concept of consensus clustering. In this paper, we provide experimental evidence to the …