# Comparing Nodes of Multivariate Graphs
Through Dynamic Layout Adaptations

P. Berger S. Beleites C. Tominski 0000-0001-7704-355X
Institute for Visual & Analytic Computing, University of Rostock, Germany

###### Abstract

Visual comparison is an important task in the analysis of multivariate graphs.
However, comparison of topological features of a graph with respect to its
data attributes for different portions of the data remains challenging because
there is no single visual representation that would suit the dynamic nature of
comparative analyses. To facilitate the visual comparison in node-link
diagrams, we propose the _comparison lens_ as a focus+context approach for
dynamic layout adaptation. The core idea is to start with a topology-driven
layout and locally inject an attribute-driven layout based on the multivariate
similarity of node attributes. This facilitates comparison tasks on a local
level while preserving the user’s overall mental map of the graph topology.
Additional visual enhancements, including color-coding, reduction of edge
clutter, and radial guides, further support the comparison. To fit the lens to
different comparison situations, it can be configured via user-controllable
parameters. To demonstrate the utility of our approach, we use it for
comparison in a real-world dataset of soccer players.

<ccs2012> <concept>
<concept_id>10003120.10003145.10003147.10010365</concept_id>
<concept_desc>Human-centered computing Visual analytics</concept_desc>
<concept_significance>500</concept_significance> </concept> </ccs2012>

[500]Human-centered computing Visual analytics

##  1 Introduction

The challenge of analyzing multivariate graphs lies in understanding the graph
topology in relation to the graph’s multivariate data attributes [KPW14]. In
order to find relationships between the graph topology and the underlying
attribute values, it is usually necessary to compare different portions of the
data with respect to varying analytic goals. For example, it can be
interesting to compare the topology of subgraphs induced by nodes that exhibit
some given attribute values. Or, vice versa, one may want to compare the
attribute value distributions of the nodes of certain given graph components.
For a comprehensive understanding of the data, several such visual comparisons
need to be conducted.

However, comparison in the context of multivariate graphs is challenging,
because it requires visual representations that encode both, the graph
topology and the multivariate attributes. In this work, we concentrate on
comparing node attributes when the basic graph visualization is a node-link
diagram. In node-link diagrams, standard topology-driven layout algorithms
(e.g., force-directed layout) make the graph topology visible. One or two
selected node attributes can additionally be visualized via on-node encoding
[NMSL19]. With this standard approach, comparing nodes regarding their
multivariate attributes is difficult. The reason is that the nodes to be
compared could be spatially far apart in the layout, which complicates their
comparison, and only very few visualized attributes can be compared directly.

Alternatively, one could use an attribute-driven layout where node coordinates
are set based on attribute values. This brings similar nodes spatially close
to each other, and hence, makes comparisons easier. However, using an
attribute-driven layout usually means that the representation of the graph
topology is compromised or even neglected altogether. Moreover, the graph
layout would change globally when different attributes are selected for the
layout computation. This would make it hard or even impossible to develop or
maintain a mental map of the overall graph. Therefore, neither topology-driven
layouts with on-node encoding nor attribute-driven layouts alone are ideal for
node comparison tasks.

Our goal is to support the flexible comparison of multivariate nodes while
keeping the overall graph topology in a node-link diagram intact. To this end,
some form of hybrid layout is required where topological aspects and
multivariate attribute values can co-exist, not globally but for a dynamically
changing focus of interest. Inspired by previous work on dynamic layout
adaptations, we propose the _comparison lens_ as a lightweight interactive
tool that can induce transient layout changes into a node-link diagram to make
comparison tasks easier. Within the lens, nodes are laid out according to
their multivariate similarity with respect to a selected focus node, which
makes their visual comparison straightforward. Thanks to the focus+context
nature of our lens, the overall topology-driven layout is changed only locally
so that the cognitive load is reduced and the overall mental map easier to
preserve.

After a brief review of related work in Section 2, we describe the
functionality of the comparison lens, additional visual enhancements, and
user-controllable parameters in detail in Section 3. We demonstrate the
utility of our technique for the case of comparing nodes in a network of
soccer players in Section 4. This paper closes with a brief summary and some
ideas for future work in Section 5.

##  2 Related Work

Our work is related to previous research in multivariate graph visualization,
visual comparison, and dynamic adaptation of visualizations.

#### Visualizing Multivariate Graphs

The visualization of multivariate graphs is comprehensively discussed in the
literature [KPW14, NMSL19]. Typically, general graph visualization techniques
such as node-link diagrams or matrix representations are extended to show the
multivariate attributes of the graph, for example via incorporating additional
views [KLS∗17, NSL19], embedding additional encodings [MB19, BST19], or laying
out the graph based on its attributes [SA06, WT08].

In this work, we focus on node-link diagrams as a common graph representation.
The literature distinguishes topology-driven and attribute-driven layouts for
placing nodes in node-link diagrams. Topology-driven layout algorithms, such
as the force-directed layout [FR91], aim to make the graph topology visible.
Attributes can then be represented via additional encodings on the graph nodes
[DS13, vdEvW14, JKS06]. On the other hand, attribute-driven algorithms lay out
nodes according to their attribute values, for example, as groups in shared
regions [SA06, RMFS∗11], in diagram-like coordinate systems [BCD∗10, EGST16],
or via multi-dimensional scaling [DCW11]. As already mentioned, topology-
driven and attribute-driven layouts have pros and cons, but comparison tasks
remain difficult, in particular when properties of the graph topology need to
be interpreted in relation to properties of attributes.

#### Visual Comparison

For visual comparison in general, Gleicher et al. [GAW∗11] propose three
fundamental approaches: juxtaposition, superposition, and direct encoding.
Superposition is about showing the elements to be compared on top of each
other, juxtaposition creates side-by-side layouts, and direct encoding is
based on calculating and visualizing differences directly. These general
approaches are also applied for supporting the visual comparison in graphs,
for example, by juxtaposed layouts [AWW09] or superposition of edge
information [ABHR∗13].

Both, juxtaposition and superposition make it easier to carry out comparison
tasks. However, juxtaposed visual representations may conflict with the user’s
mental map of the graph topology. For superposition, clutter can be an issue
and could lead to misrepresentation of the data. Direct encoding has the
advantage that differences are shown directly. However, the visualization of
differences may be in conflict with the visualization of the graph itself.
Given these different pros and cons, Tominski et al. [TFJ12] suggest natural
interaction techniques allowing users to dynamically create comparison layouts
that best suit their task at hand. Here, we pick up the idea of dynamically
adapting a visualization on demand.

#### Dynamic Adaptation of Visualizations

The goal of dynamic visualization adaptations is to provide flexible access to
different perspectives on the data while maintaining the general context
[BBD20, TAA∗21, HBS∗21]. Instead of abrupt switches between views, smooth
transitions take users from one perspective to another to keep users in the
flow, as proposed by Elmqvist et al. [EMJ∗11].

Focus+context approaches have long since been used for dynamic adaptation of a
region of interest [CKB08]. Interactive lenses are a prominent example of
focus+context techniques. Lens techniques are defined as lightweight tools
that induce transient changes into an existing base visualization [TGK∗17].

In node-link diagrams, lenses can dynamically adapt the layout to create local
neighborhood overviews [TAS09] or support navigation via _bring & go_
[MCH∗09]. Lenses can also be used to reveal additional details about node
attributes [JDK10]. Lens-like techniques have also been used to support visual
comparison. For example, the _CompaRing_ implements a _bring & compare_
strategy that creates an on-demand juxtaposition of geographic regions to be
compared [Tom16].

Inspired by these previous works, we propose a lens technique that dynamically
adapts the layout of node-link diagrams to support the visual comparison of
node attributes in multivariate graphs. To our knowledge, such a dynamic
layout adaptation for visual comparison has so far not been described in the
visualization literature.

##  3 Approach

Next, we briefly discuss the requirements of our approach and present the
basic idea for a _comparison lens_ to facilitate comparison tasks in node-link
diagrams. In addition to the dynamic layout adaptation, we include visual
enhancements to further support the comparison and parameters to tune the lens
to the situation at hand.

Figure 1: Schematic illustration of the lens-based dynamic layout adaptation
(left) and visual enhancements (right) for comparison of nodes. Left: The
focus node is centered (red arrow), nodes that meet the similarity threshold
are placed according to their similarity with respect to the focus (green
arrows), and nodes that do not meet the similarity threshold are moved out of
the lens (blue arrows). Right: Different shades of green indicate attribute-
based node similarity and radial guides are embedded in the lens to further
support the visual comparison.

###  3.1 Requirements

Based on the problem description and the review of related work, we identified
the following requirements for our approach:

R1 Support Visual Comparison:

Given an existing node-link diagram, it should be possible to effortlessly
compare selected nodes according to their attribute values.

R2 Preserve Mental Map:

The user’s mental map of the global graph topology should be preserved by
reducing global layout changes to a necessary minimum.

R3 Smooth display transitions:

To keep users in the flow, smooth display transitions should be favored over
disruptive view switches.

###  3.2 Approach Overview

The general idea of our approach is to locally induce an attribute-driven
layout into an otherwise topology-driven layout of a multivariate graph.
Accordingly, we start with a force-directed node-link diagram that represents
the graph topology in a zoomable view. Node size and color can optionally be
used to visualize selected node attributes.

To facilitate the comparison of nodes and their attributes in relation to the
graph topology as demanded by R1, we propose a lens technique that transforms
the graph layout locally based on node attribute values. Addressing R2, the
attribute-driven layout change is restricted locally to the lens interior so
that the topology-driven layout is mostly preserved. To satisfy R3, nodes that
are relevant for a comparison task are pulled into the lens and irrelevant
nodes are pushed out of the lens via smooth transitions.

Next, we describe how exactly nodes are placed in the lens and how additional
visual enhancements and controllable lens parameters further support the
comparison.

###  3.3 Lens-based Dynamic Layout Adaptation

The lens is defined by a focal point and a radius around it. When moving the
lens across the node-link diagram, the nearest node is centered within the
lens and becomes the focus node, shown in red in Figure 1. Alternatively, a
focus node can be selected using the cursor and the lens is automatically
positioned on top of it.

Once a focus node has been specified, the lens interior transforms to an
attribute-driven layout that facilitates the comparison of the focus node with
other relevant nodes. Different plausible criteria exist to determine which
other nodes should be deemed relevant. One could consider the topological
$k$-neighborhood of the focus node or the view neighborhood within a certain
window around the lens. As we are primarily interested in node attributes, we
use the “attribute neighborhood” of the focus node, which boils down to
considering the similarity of nodes according to their multivariate attribute
values.

To this end, the pairwise attribute-based similarity of the focus node to all
other nodes is calculated. This can be done in different ways, for example,
based on average Euclidean distance, Cosine similarity, or Pearson correlation
coefficient. Which nodes are considered relevant is then determined based on a
user-adjustable similarity threshold. As indicated by green arrows in Figure
1, nodes that satisfy the threshold, and are thus of interest to the user, are
re-positioned inside the lens. Nodes that are most similar to the focus node
are pulled close toward it. Nodes that just meet the similarity threshold are
positioned on the lens boundary. Moreover, nodes that do not meet the
similarity threshold are moved slightly beyond the lens border as shown with
blue arrows in Figure 1. The pulling of relevant nodes toward the lens and the
pushing of irrelevant nodes out of the lens can be considered a combination of
previously described lens effects from the BringNeighbors lens [TAvS06] and
the MoleView lens [HTE11]. The layout adaptation is done in a smooth, force-
based animation to make it easy to understand.

In the resulting local attribute-driven layout, the distance of nodes to the
lens center represents the similarity of nodes with respect to the focus node.
As all nodes being considered relevant for the comparison are placed inside
the lens, they can now be compared based on their distance to the focus node
and to each other. By moving the lens or by selecting another focus node
(e.g., from among the re-located nodes), users may continue their comparative
exploration of the graph.

While the dynamic layout adaptation already makes comparison tasks easier, it
can still require some cognitive effort to compare the distances between
nodes. In the following, we discuss visual enhancements to further support the
comparison.

###  3.4 Visual Enhancements

To compare the similarity of two nodes, their corresponding distances to the
lens center must be compared. This can be difficult, if the two nodes to be
compared are far apart in the lens. To address this issue, users can
optionally activate additional visual cues to support the comparison. A first
option is to color-code the similarity value on the nodes as show in the right
part of Figure 1.

Secondly, concentric circles can be embedded in the lens, acting as radial
guides similar to the approach by Moscovich et al. [MCH∗09]. Users can select
between three different modes: (i) fixed equidistant circles, (ii) fixed
circles per node inside the lens, and (iii) a dynamic circle that is linked to
the cursor and optionally snaps to nodes. The radial guides make it much
easier to compare nodes with respect to their distance to the center, as can
also be seen in Figure 1 (right), in particular for the brighter nodes close
to the lens border. The different modes for the radial guides allow users to
apply them in a task-specific manner. Exploratory analysis phases could be
supported with a dynamic circle, whereas a few fixed circles can serve
confirmatory or presentation purposes.

A final visual enhancement addresses the in-lens clutter of the regular edges
of the node-link diagram, which can be severe especially for larger or denser
graphs. To clear potential edge clutter, we combine our lens with an
additional lens effect, originally proposed as _LocalEdge_ lens by Tominski et
al. [TAvS06]. The added effect cuts off edges at the border of the lens if
they do not connect to nodes within the lens. This effectively means that the
lens shows only those edges that have an incident node inside the lens. In
some situations, it could even be helpful to require both incident nodes of an
edge to be within the lens.

The described visual enhancements as well as the dynamic layout adaptation and
the lens itself can be fine-tuned via user-controllable parameters, which will
be described next.

###  3.5 User-controllable Lens Parameters

Our comparison lens is an interactive tool and as such offers several options
for users to control the lens appearance and function. Most importantly, users
can control the focus node, which is closely related to the lens position, the
attributes to be used for the similarity calculation, the similarity
threshold, the lens size, as well as the mode for the radial guides.

To satisfy changing information needs of users, it is a basic functionality to
adjust the lens position via direct-manipulation drag gestures. As the lens is
moved, the node nearest to the lens center is automatically selected as the
focus node. Alternatively, the focus node can be selected via a click, upon
which the lens is automatically centered on the new focus node.

To further adapt the lens to different comparison goals, users can
interactively specify via a dedicated user interface which node attributes
should be taken into account for the similarity calculation. Users may express
their interest in individual attributes and also according to any multivariate
combination of attributes. Changing the attribute selection of the lens
results in a re-calculation of all pairwise similarities with respect to the
current focus node. Once the calculation is complete, a smooth display
transition updates the node layout inside the lens.

Related to the similarity calculation is the adjustment of the similarity
threshold. It determines how many nodes are considered relevant to be affected
by the layout adaptation. Currently, the similarity threshold can be set via a
slider in the user interface. For a more direct adjustment of this important
parameter, it could also make sense to consider on-lens controls as suggested
by Kister et al. [KRD16].

Finally, users can adjust the size of the lens through direct manipulation.
This way, it is possible to balance the display space dedicated to the
attribute-based visual comparison in the focus inside the lens and the
preservation of the graph topology in the context outside the lens. Moreover,
the lens size determines the precision with which the similarity of nodes can
be discerned from the attribute-driven layout in the lens interior. If the
comparison task is central to the data analysis a large lens with higher
precision can be used. For brief comparisons, a smaller lens with lower
precision may suffice.

In summary, our comparison lens is a novel interactive tool to support on-the-
fly visual comparison in multivariate graphs through dynamic layout adaptation
and visual enhancements. Several parameters are available to adjust the lens.
Next, we illustrate the utility of our lens for a real-world dataset.

##  4 Use Case

(a)

(b)

(c)

Figure 2: Applying the comparison lens to a graph of soccer players. (a) Basic
node-link diagram showing the graph topology. (b) Activating the lens pushing
irrelevant nodes out of the lens, whereas similar, and hence, relevant nodes
are positioned inside the lens based on their similarity. (c) Through edge
clutter reduction and radial guides, the comparison inside the lens is
enhanced.

For a brief demonstration, we use a multivariate graph of soccer players from
the Champions League season 2017/18. Each node of the graph represents a
player with multivariate attributes capturing the players’ offensive and
defensive properties (e.g., minutes played, ball possession, shots on goal,
scored goals). Edges exist between players that have played in the same club.
The number of clubs in which two plays have played is captured as the edge
weight. Overall, the graph has 95 nodes, 1046 edges, and 39 quantitative node
attributes. The basic node-link diagram in Figure 2(a) shows the graph
topology. Node degree is visualized by node size and shades of blue. Edge
weight is encoded in the link width.

As a team manager, one goal of a comparative data analysis could be to find
new players that would fit well in our club. In our example, we want to find
an additional goalkeeper. For this, we first select our present goalkeeper as
the focus node (light blue node with black outline in Figure 2(a)). The
relevant attributes for goalkeepers to be taken into account for the
similarity calculation are set to be keeper_missed and keeper_save_total.

Then we activate the comparison lens in Figure 2(b). Players that do not meet
the similarity threshold of 50% are pushed out of the lens. Players being
sufficiently similar to our selected goalkeeper are moved into the lens and
are colored in shades of green to indicate their similarity with respect to
the focus node. As can be seen, five goalkeepers are brought to the lens. Two
of them are positioned particularly close to the lens center, indicating high
similarity. These two players might be interesting as goalkeeper for our team.

For a more detailed look at the situation, we further activate visual
enhancements in Figure 2(c). The reduced edge clutter improves the visibility
of relevant edges in the lens and makes it easier to investigate the
connectivity of similar nodes. Moreover, the radial guides improve the
interpretation of the similarity. We thus conclude that of the two candidates
(dark green nodes), the lower one is slightly more similar and hence should be
the player to be approached first for a new contract.

##  5 Conclusion

Addressing the challenging task of visual comparison in multivariate graphs,
we proposed the _comparison lens_ as a novel focus+context approach for
dynamic layout adaptation to facilitate comparison of nodes in node-link
diagrams. The core idea is to dynamically create a layout where topological
features and multivariate attribute characteristics are smoothly intertwined.
Accordingly, nodes are positioned inside the lens with respect to their
multivariate similarity, while a topological layout is preserved outside the
lens. This way, cognitive load can be reduced for comparison tasks and the
mental map can be preserved easier compared to disruptive view switches.
Additional visual enhancements are incorporated into the lens to further
support the comparison. Users can adapt the lens to their comparison tasks via
controllable parameters.

In the future, we plan to further improve the utility of our approach. One
possible avenue for future work is to enhance the presentation of data
attributes within the lens. Currently, the nodes are color-coded according to
their similarity. However, how individual attributes contribute to the
similarity is not visible. Here it can make sense to dynamically embed
additional information. For example, one could transform the simple dots into
multivariate glyphs representing the attributes used for the similarity
calculation.

Finally, in order to quantitatively evaluate the utility of our approach, a
user study comparing task performance for visual comparison among plain
topology-driven layout, an attribute-driven layout, and our dynamic lens-
driven hybrid layout would be a worthwhile goal for future work.

## References

  * [ABHR∗13] Alper B., Bach B., Henry Riche N., Isenberg T., Fekete J.-D.:  Weighted Graph Comparison Techniques for Brain Connectivity Analysis.  In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI)_ (2013), ACM Press.  doi:10.1145/2470654.2470724.
  * [AWW09] Andrews K., Wohlfahrt M., Wurzinger G.:  Visual Graph Comparison.  In _Proceedings of the International Conference Information Visualisation (IV)_ (2009), IEEE Computer Society.  doi:10.1109/IV.2009.108.
  * [BBD20] Brüggemann V., Bludau M.-J., Dörk M.:  The Fold: Rethinking Interactivity in Data Visualization.  _Digital Humanities Quarterly 14_ , 3 (2020).  URL: http://www.digitalhumanities.org/dhq/vol/14/3/000487/000487.html.
  * [BCD∗10] Bezerianos A., Chevalier F., Dragicevic P., Elmqvist N., Fekete J.:  GraphDice: A System for Exploring Multivariate Social Networks.  _Computer Graphics Forum 29_ , 3 (2010).  doi:10.1111/j.1467-8659.2009.01687.x.
  * [BST19] Berger P., Schumann H., Tominski C.:  Visually Exploring Relations between Structure and Attributes in Multivariate Graphs.  In _Proceedings of the International Conference Information Visualisation (IV)_ (2019), IEEE Computer Society.  doi:10.1109/IV.2019.00051.
  * [CKB08] Cockburn A., Karlson A., Bederson B. B.:  A Review of Overview+Detail, Zooming, and Focus+Context Interfaces.  _ACM Computing Surveys 41_ , 1 (2008).  doi:10.1145/1456650.1456652.
  * [DCW11] Dörk M., Carpendale S., Williamson C.:  EdgeMaps: Visualizing Explicit and Implicit Relations.  In _Proceedings of the Conference on Visualization and Data Analysis (VDA)_ (2011), vol. 7868 of _SPIE Proceedings_ , SPIE, p. 78680G.  doi:10.1117/12.872578.
  * [DS13] Dunne C., Shneiderman B.:  Motif Simplification: Improving Network Visualization Readability with Fan, Connector, and Clique Glyphs.  In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI)_ (2013), ACM Press.  doi:10.1145/2470654.2466444.
  * [EGST16] Eichner C., Gladisch S., Schumann H., Tominski C.:  Direct Visual Editing of Node Attributes in Graphs.  _Informatics 3_ , 4 (2016).  doi:10.3390/informatics3040017.
  * [EMJ∗11] Elmqvist N., Moere A. V., Jetter H.-C., Cernea D., Reiterer H., Jankun-Kelly T.:  Fluid Interaction for Information Visualization.  _Information Visualization 10_ , 4 (2011).  doi:10.1177/1473871611413180.
  * [FR91] Fruchterman T. M., Reingold E. M.:  Graph Drawing by Force-Directed Placement.  _Software: Practice and Experience 21_ , 11 (1991).  doi:10.1002/spe.4380211102.
  * [GAW∗11] Gleicher M., Albers D., Walker R., Jusufi I., Hansen C. D., Roberts J. C.:  Visual Comparison for Information Visualization.  _Information Visualization 10_ , 4 (2011).  doi:10.1177/1473871611416549.
  * [HBS∗21] Horak T., Berger P., Schumann H., Dachselt R., Tominski C.:  Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs.  _IEEE Transactions on Visualization and Computer Graphics 27_ , 2 (2021).  doi:10.1109/TVCG.2020.3030371.
  * [HTE11] Hurter C., Telea A. C., Ersoy O.:  MoleView: An Attribute and Structure-Based Semantic Lens for Large Element-Based Plots.  _IEEE Transactions on Visualization and Computer Graphics 17_ , 12 (2011).  doi:10.1109/TVCG.2011.223.
  * [JDK10] Jusufi I., Dingjie Y., Kerren A.:  The Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering.  In _Proceedings of the International Conference Information Visualisation (IV)_ (2010), IEEE Computer Society.  doi:10.1109/IV.2010.15.
  * [JKS06] Junker B. H., Klukas C., Schreiber F.:  VANTED: A System for Advanced Data Analysis and Visualization in the Context of Biological Networks.  _BMC Bioinformatics 7_ , 1 (2006), 109.  doi:10.1186/1471-2105-7-109.
  * [KLS∗17] Kerzner E., Lex A., Sigulinsky C., Urness T., Jones B., Marc R., Meyer M.:  Graffinity: Visualizing Connectivity in Large Graphs.  _Computer Graphics Forum 36_ , 3 (2017).  doi:10.1111/cgf.13184.
  * [KPW14] Kerren A., Purchase H. C., Ward M. O. (Eds.):.  _Multivariate Network Visualization_ (2014), vol. 8380 of _Lecture Notes in Computer Science_ , Springer.  doi:10.1007/978-3-319-06793-3.
  * [KRD16] Kister U., Reipschläger P., Dachselt R.:  MultiLens: Fluent Interaction with Multi-Functional Multi-Touch Lenses for Information Visualization.  In _Proceedings of the International Conference on Interactive Surfaces and Spaces (ISS)_ (2016), ACM Press.  doi:10.1145/2992154.2992168.
  * [MB19] Major T., Basole R. C.:  Graphicle: Exploring Units, Networks, and Context in a Blended Visualization Approach.  _IEEE Transactions on Visualization and Computer Graphics 25_ , 1 (2019).  doi:10.1109/tvcg.2018.2865151.
  * [MCH∗09] Moscovich T., Chevalier F., Henry N., Pietriga E., Fekete J.-D.:  Topology-Aware Navigation in Large Networks.  In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI)_ (2009), ACM Press.  doi:10.1145/1518701.1519056.
  * [NMSL19] Nobre C., Meyer M., Streit M., Lex A.:  The State of the Art in Visualizing Multivariate Networks.  _Computer Graphics Forum 38_ , 3 (2019).  doi:10.1111/cgf.13728.
  * [NSL19] Nobre C., Streit M., Lex A.:  Juniper: A Tree+Table Approach to Multivariate Graph Visualization.  _IEEE Transactions on Visualization and Computer Graphics 25_ , 1 (2019).  doi:10.1109/tvcg.2018.2865149.
  * [RMFS∗11] Rodrigues E. M., Milic-Frayling N., Smith M., Shneiderman B., Hansen D.:  Group-in-a-Box Layout for Multi-faceted Analysis of Communities.  In _Proceedings of the Third International Conference on Privacy, Security, Risk and Trust and Third International Conference on Social Computing_ (2011), IEEE.  doi:10.1109/PASSAT/SocialCom.2011.139.
  * [SA06] Shneiderman B., Aris A.:  Network Visualization by Semantic Substrates.  _IEEE Transactions on Visualization and Computer Graphics 12_ , 5 (2006).  doi:10.1109/TVCG.2006.166.
  * [TAA∗21] Tominski C., Andrienko G., Andrienko N., Bleisch S., Fabrikant S. I., Mayr E., Miksch S., Pohl M., Skupin A.:  Toward Flexible Visual Analytics Augmented through Smooth Display Transitions.  _Visual Informatics 5_ , 3 (2021).  doi:10.1016/j.visinf.2021.06.004.
  * [TAS09] Tominski C., Abello J., Schumann H.:  CGV – An Interactive Graph Visualization System.  _Computers & Graphics 33_, 6 (2009).  doi:10.1016/j.cag.2009.06.002.
  * [TAvS06] Tominski C., Abello J., van Ham F., Schumann H.:  Fisheye Tree Views and Lenses for Graph Visualization.  In _Proceedings of the International Conference Information Visualisation (IV)_ (2006), IEEE Computer Society.  doi:10.1109/IV.2006.54.
  * [TFJ12] Tominski C., Forsell C., Johansson J.:  Interaction Support for Visual Comparison Inspired by Natural Behavior.  _IEEE Transactions on Visualization and Computer Graphics 18_ , 12 (2012).  doi:10.1109/TVCG.2012.237.
  * [TGK∗17] Tominski C., Gladisch S., Kister U., Dachselt R., Schumann H.:  Interactive Lenses for Visualization: An Extended Survey.  _Computer Graphics Forum 36_ , 6 (2017).  doi:10.1111/cgf.12871.
  * [Tom16] Tominski C.:  CompaRing: Reducing Costs of Visual Comparison.  In _Short Paper Proceedings of the Eurographics Conference on Visualization (EuroVis)_ (Groningen, NL, 2016), Eurographics Association.  doi:10.2312/eurovisshort.20161175.
  * [vdEvW14] van den Elzen S., van Wijk J. J.:  Multivariate Network Exploration and Presentation: From Detail to Overview via Selections and Aggregations.  _IEEE Transactions on Visualization and Computer Graphics 20_ , 12 (2014).  doi:10.1109/tvcg.2014.2346441.
  * [WT08] Wu Y., Takatsuka M.:  Visualizing Multivariate Networks: A Hybrid Approach.  In _Proceedings of the IEEE Pacific Visualization Symposium (PacificVis)_ (2008), IEEE Computer Society.  doi:10.1109/pacificvis.2008.4475480.