Publications in Scientific Journals:

H. Wu, S. Takahashi et al.:
"Overlap-Free Labeling of Clustered Networks Based on Voronoi Tessellation";
Journal of Visual Languages and Computing (invited), 44 (2018), 106 - 119.

English abstract:
Properly drawing clustered networks significantly improves the visual readability of the meaningful struc-
tures hidden behind the associated abstract relationships. Nonetheless, we often degrade the visual qual-
ity of such clustered graphs when we try to annotate the network nodes with text labels due to their
unwanted mutual overlap. In this paper, we present an approach for aesthetically sparing labeling space
around nodes of clustered networks by introducing a space partitioning technique. The key idea of our
approach is to adaptively blend an aesthetic network layout based on conventional criteria with that ob-
tained through centroidal Voronoi tessellation. Our technical contribution lies in choosing a specific dis-
tance metric in order to respect the aspect ratios of rectangular labels, together with a new scheme for
adaptively exploring the proper balance between the two network layouts around each node. Centrality-
based clustering is also incorporated into our approach in order to elucidate the underlying hierarchical
structure embedded in the given network data, which also allows for the manual design of its overall
layout according to visual requirements and preferences. The accompanying experimental results demon-
strate that our approach can effectively mitigate visual clutter caused by the label overlaps in several
important types of networks.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.