Multivariate networks are made up of nodes and their relationships (links), but also data
about those nodes and links as attributes. Most real‐world networks are associated with …

Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and
financial networks are some examples. Often these networks are large and multivariate …

Graph visualization is an important field in information visualization that is centered on the
graphical display of graph-structured data. Yet real world data is rarely just graph-structured …

Abstract Information visualization focuses on the use of techniques to help people
understand and analyze data. This chapter puts multivariate networks in the context of the …

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that
facilitates exemplar-based adjustment of a group of substructures in batching mode. The key …

Node-link diagrams are widely used to visualize graphs. Most graph layout algorithms only
use graph topology for aesthetic goals (eg, minimize node occlusions and edge crossings) …

Hierarchical abstraction is a scalable strategy to deal with large networks. Existing
visualization methods have allowed to aggregate the network nodes into hierarchies based …

Uncovering causal relations from event sequences to guide decision‐making has become
an essential task across various domains. Unfortunately, this task remains a challenge …

Interaction is a vital component in the visualization of multivariate networks. It enables
greater amounts of information to be seen and explored than is possible with static …

It is a challenging task to explore large-scaled multivariate networks, since nodes of
multivariate networks always contain rich information. We propose a novel visual abstraction …