Undirected graphs are frequently used to model phenomena that deal with interacting
objects, such as social networks, brain activity and communication networks. The topology of …

Depending on the node ordering, an adjacency matrix can highlight distinct characteristics
of a graph. Deriving a “proper” node ordering is thus a critical step in visualizing a graph as …

In this study, we examine the use of graph ordering algorithms for visual analysis of data
sets using visual similarity matrices. Visual similarity matrices display the relationships …

Matrix representations are one of the main established and empirically proven to be effective
visualization techniques for relational (or network) data. However, matrices-similar to node …

Stacked graphs have been widely adopted in various fields, because they are capable of
hierarchically visualizing a set of temporal sequences as well as their aggregation …

Visualizing graphs is a challenging task due to the various properties of the underlying
relational data. For sparse and small graphs the perceptually most efficient way are node …

This survey provides a description of algorithms to reorder visual matrices of tabular data
and adjacency matrix of Networks. The goal of this survey is to provide a comprehensive list …

How can we efficiently find clusters (aka communities) included in a graph with millions or
even billions of edges? Density-based graph clustering SCAN is one of the fundamental …

Data analysts commonly utilize statistics to summarize large datasets. While it is often
sufficient to explore only the summary statistics of a dataset (eg, min/mean/max) …

Graph and network visualization is a well-researched area. However, graphs are limited in
that by definition they are designed to encode pairwise relationships between the nodes in …