HoloGraphs: An Interactive Physicalization for Dynamic Graphs

Daniel Pahr

, Henry Ehlers

and Velitchko Filipov

TU Wien, Austria

Keywords:

Physicalization, Fabrication, Information Visualization, Dynamic Networks.

Abstract:

We present HoloGraphs, a novel approach for physically representing, explaining, exploring, and interacting

with dynamic networks. HoloGraphs addresses the challenges of visualizing and understanding evolving net-

work structures by providing an engaging method of interacting and exploring dynamic network structures

using physicalization techniques. In contrast to traditional digital interfaces, our approach leverages tangible

artifacts made from transparent materials to provide an intuitive way for people with low visualization literacy

to explore network data. The process involves printing network embeddings on transparent media and assem-

bling them to create a 3D representation of dynamic networks, maintaining spatial perception and allowing the

examination of each timeslice individually. Interactivity is envisioned using optional Focus+Context layers

and overlays for node trajectories and labels. Focus layers highlight nodes of interest, context layers provide an

overview of the network structure, and global overlays show node trajectories over time. In this paper, we out-

line the design principles and implementation of HoloGraphs and present how elementary digital interactions

can be mapped to physical interactions to manipulate the elements of a network and temporal dimension in an

engaging matter. We demonstrate the capabilities of our concept in a case study. Using a dynamic network of

character interactions from a popular book series, we showcase how it represents and supports understanding

complex concepts such as dynamic networks.

1 INTRODUCTION

Dynamic network visualization aims to support ex-

tracting insights and making sense of dynamically

changing network structures (Beck et al., 2017). Sub-

sequently, dynamic graphs and their visualization

have found common use across a variety of domains,

from the social sciences (Oelke et al., 2013), through

software engineering (Ruﬁange and Melanc¸on, 2014),

to metabolic pathway analysis (Rohrschneider et al.,

2010). Several approaches to visualizing such graphs

have been proposed, such as animated node-link di-

agrams (Hayashi et al., 2013), layered matrix repre-

sentations (Vehlow et al., 2013), integrated 1.5D rep-

resentations (Shi et al., 2015), or a superimposition

of multiple 2D embeddings from different points in

time (Filipov et al., 2024). With multiple embeddings

for each point in time, we can produce space-time

cubes (2D + t or 3D), that encode the temporal as-

pect in a third dimension. However, virtual 3D rep-

resentations suffer from occlusions, distortions, and

parallax effects (Bach et al., 2014). This can, in turn,

obscure the graph’s topology and change over time,

https://orcid.org/0000-0001-7313-3056

https://orcid.org/0000-0002-5994-1492

https://orcid.org//0000-0001-9592-2179

negatively impacting user perception and understand-

ing. In contrast, by arranging the slices side-by-side,

i.e. juxtaposing them, we lose the ability to perceive

nodes’ movement over time between the individual

timeslices (Beck et al., 2017; Ehlers et al., 2024a; Fil-

ipov et al., 2023a). A key beneﬁt of data physicaliza-

tion is that we can examine 3D representations tangi-

bly and intuitively by physically manipulating them.

Research shows that certain drawbacks of 3D repre-

sentations on screens can be overcome by such physi-

calizations (Jansen et al., 2013). Speciﬁcally, research

regarding networks in physical space has focused on

both 2D embeddings with additional tactile encod-

ings (Drogemuller et al., 2021) as well as 3D em-

beddings with added interactivity (Bae et al., 2024).

Drogemuller et al.(Drogemuller et al., 2021) present

their ﬁndings about physicalized graphs being more

engaging and fun to interact with, compared to their

screen-based counterpart.

Given these outcomes regarding engagement and

fun, it is unsurprising that interactive data physi-

calizations have value particularly related to educa-

tion and visualization literacy (O’Malley and Fraser,

2004). Manipulable physical representations have

great value in educational settings, conceptually al-

lowing for greater engagement, understanding, and

Pahr, D., Ehlers, H. and Filipov, V.

HoloGraphs: An Interactive Physicalization for Dynamic Graphs.

DOI: 10.5220/0013116000003912

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2025) - Volume 1: GRAPP, HUCAPP

and IVAPP, pages 859-866

ISBN: 978-989-758-728-3; ISSN: 2184-4321

859

learning (Jansen et al., 2015; Jansen and Dragicevic,

2013; Pahr et al., 2024). We argue that there still

are numerous opportunities to leverage the unique

strengths of data physicalization to communicate

complex scientiﬁc phenomena and concepts, such as

dynamic graphs and space-time cubes.

We introduce a novel workﬂow to create interac-

tive, physical representations of dynamic networks.

We make this workﬂow accessible by moving the fo-

cus away from complex 3D-printing technologies and

toward widely and cheaply available materials that

can be assembled with various means. By printing

network embeddings on overhead projector slides, we

obtain transparent slices that can be assembled in par-

allel to create a 3D appearance: a HoloGraph. This

allows us to keep the spatial perception of the space-

time cube while enabling the examination of individ-

ual timeslices at any point. We divide each of the net-

work’s embeddings into focus slices that highlight and

track nodes of interest and context slices that depict

the contextual structure of the network at different

points in time. We also provide separate global slices

such as label overlays for nodes of interest and tra-

jectory overlays to track nodes’ movement over time.

The codebase, printable versions of our networks, as

well as 3D-printable meshes we used for our sculp-

tures, can be found online

. We present a demonstra-

tion of our approach’s utility in a case study, exploring

the evolving relationships of characters across books

of the “Harry Potter” series. In summary, the contri-

butions of this work are two-fold:

• We present the development of a novel workﬂow

with which to create physical and interactive repre-

sentations of dynamic graphs.

• We demonstrate HoloGraphs’ utility in a case study

to highlight the value and importance of engage-

ment when learning about concepts such as dy-

namic graphs.

2 RELATED WORK

Data Physicalization. Data physicalization trans-

forms abstract data into tangible forms, leveraging

the physical properties of artifacts and materials to

encode information (Jansen et al., 2015). This ap-

proach has shown promise in making data more ac-

cessible and engaging. For instance, 3D physical rep-

resentations can outperform their screen-based coun-

terparts in efﬁciency for certain tasks (Jansen et al.,

https://osf.io/4u2e9/?view only=

751235378e564086beee9de8d37a6686

2013). Furthermore, they can also enhance mem-

ory retention and engagement compared to virtual

approaches (Stusak et al., 2015; Hurtienne et al.,

2020). Data physicalization approaches often include

interactive elements (“hands-on”), allowing users to

engage with the data actively, performing common

tasks such as ﬁltering and selecting interesting data

items (Brehmer and Munzner, 2013). For instance,

Vol2Velle (Stoppel and Bruckner, 2017) allows the

selection of transfer function parameters by rotating

disks. Similarly, in Volograms (Pahr et al., 2021) in-

dividual slices can be removed to be inspected indi-

vidually or to examine otherwise obstructed regions.

Schindler et al. (Schindler et al., 2020) propose the

use of color ﬁlters to allow the ﬁltering of differ-

ent anatomical regions. Squishicalization (Pahr et al.,

5555) encodes scalar ﬁeld data into elastic 3D-printed

sculptures. Bae et al. (Bae et al., 2024) propose a

pipeline for creating network physicalizations that al-

low simple interactions with the nodes of the network

using electrical circuitry.

Physicalizing Networks. Networks are powerful

structures used to model and visualize data as a set

of entities and relationships between them. They are

widely employed in different domains to help under-

stand connections, detect patterns, and identify inﬂu-

ential actors (Lee et al., 2006; Jae-wook Ahn et al.,

2014). Physicalizing networks adds a tactile dimen-

sion to the data exploration and analysis process, en-

hancing accessibility for the visually impaired indi-

viduals (Drogemuller et al., 2021; McGookin et al.,

2010). Recent research investigates how combining

visual and haptic exploration of physical node-link

diagrams beneﬁts the understanding of such struc-

tures (Drogemuller et al., 2021) and how physical-

ized networks can support a better spatial percep-

tion through tangible interactions (McGufﬁn et al.,

2023). There is growing interest in exploring 3D im-

mersive environments for network visualization (Kot-

larek et al., 2020; Sorger et al., 2019; Oh-Hyun Kwon

et al., 2016; Colin Ware et al., 2008; Filipov et al.,

2023b). Such novel representations and interaction

techniques promise new ways to engage with the data.

Integrating dynamic network visualization with data

physicalization offers a tangible, interactive method

to explore complex concepts such as evolving net-

work structures, and behavior over time, and identi-

fying patterns and trends. Existing physicalizations

of networks often focus on simple, static structures.

However, dynamic networks introduce an additional

layer of complexity, considering the temporal evo-

lution of the entities and their relationships; an as-

pect that is crucial for understanding behavior over

IVAPP 2025 - 16th International Conference on Information Visualization Theory and Applications

860

Figure 1: A HoloGraph. We display a dynamic graph by

producing and embedding the individual timeslices, print-

ing them on transparent media, and arranging them equally

spaced. An overlay shows interesting nodes’ trajectories

over time and per individual timeslice.

time (Jae-wook Ahn et al., 2014). Approaches typi-

cally tackle this problem by aggregating (or timeslic-

ing) the network’s temporal dimension (Archambault

et al., 2011; Bach et al., 2017), while continuous rep-

resentations (event-based) capture changes occurring

at ﬁner temporal granularities (Simonetto et al., 2018;

Arleo et al., 2022; Filipov et al., 2024).

Accessible Fabrication. Digital fabrication, i.e. 3D

printing technology, is one of the most common ap-

proaches to creating 3D artifacts (Djavaherpour et al.,

2021). However, more affordable methods exist that

can make them accessible to a broader audience.

Stoppel and Bruckner (Stoppel and Bruckner, 2017),

for example, use transparent disks to create interac-

tive volume visualizations, while Pahr et al. (Pahr

et al., 2021) present hologram-like structures from

segmented volumetric data. Raidou et al. (Raidou

et al., 2020) demonstrate how volumetric data can

be printed on transparent material to create 3D sculp-

tures without the use of sophisticated technology and

Schindler et al. (Schindler et al., 2022) show methods

for creating nested paper structures for anatomical ed-

ucation. Such approaches do not require expensive

hardware or technology, resulting in more affordable

and accessible physical data representations.

3 HoloGraphs

The physicalization of node-link diagrams is conven-

tionally done using 3D-printing techniques (Bae et al.,

2024). However, such representations limit the num-

ber of nodes that can be printed and hence displayed

owing to the time and cost associated with 3D print-

ing; a limitation we here aim to side-step. Here, Pahr

et al. (Pahr et al., 2021) propose methods to process

and (interactively) display medical volumetric data

using regular printers and transparent printable me-

dia. Moreover, outside of the context of data physical-

ization, Filipov et al. (Filipov et al., 2024) describe a

method to visualize dynamic networks by projecting

the space-time cube embedding to a 2D representa-

tion. This 2D representation is an orthogonal projec-

tion of the network’s topology over time in order to

visualize node trajectories and behavior. Here, com-

bining these two approaches, we propose to show the

individual timeslices created by such a method in a

3D environment by printing each time-slice-subgraph

embedding on transparent media and arranging them

as parallel slices. To add interactivity, we propose the

use of various removeable overlays with which to dis-

play added context, draw focus, and provide global

characteristics of the data. Figure 2 provides a sim-

pliﬁed overview of the proposed process.

Deﬁnitions. Given a dynamic graph, formulated here

as a time-sliced graph G = (V

, E

, T), where V the

total set of nodes and E the total set of undirected

links across a set of timeslices T . For some timeslice

t ∈ T node-time-slice pairs (v

, t) and (v

, t) in V

are

connected by links E

⊆ V

× V

, where V

⊆ V × T .

For a set of focus nodes V

⊆ V , we aim to highlight

their position in the different timeslices over a dis-

joint set of context nodes V

⊆ V . First, the dynamic

super graph G (Figure 2a) is separated into its con-

stituent time-sliced subgraphs G

(Figure 2b). These

individual subgraphs are then each further broken up

into their individual Focus+Context subgraphs, G

and G

respectively, such that E

= V

×V

, as well

as E

= V

× V

(Figure 2c). These Focus+Context

subgraphs are then individually printed and mounted

in a physical rack, forming (together with an addi-

tional labeled overlay that is printed along nodes’

time-dependent trajectories) a tactile and interactive

dynamic graph physicalization: the HoloGraph (Fig-

ure 2d).

Virtual Embedding. The layout of the network is

computed using D3.js’s particle-based force-directed

layout algorithm (Bostock et al., 2011). Here, the key

challenge of laying out a dynamic network is to strike

a balance between having enough change in node

placement between timeslices to effectively reﬂect the

network’s evolution over time, while also restricting

said movement sufﬁciently in order to ensure layout

and network structure are still preserved. Brandes

et al. (Brandes et al., 2012) present several strategies

for doing so, most notably aggregation and anchor-

ing. In HoloGraphs we make use of an anchoring

HoloGraphs: An Interactive Physicalization for Dynamic Graphs

861

(a) Super Graph (b) Timeslice Subgraphs (c) Focus+Context Subgraphs

(d) HoloGraph

Figure 2: A dynamic graph (a), where connections between nodes and links differ between different points in time, is split up

into timeslices t

, t

, representing the state of the network at different points in time (b). To emphasize nodes

of interest ( x ), we divide the timeslices into focus (left) and context (right) subgraphs (c). Arranging the slices in parallel

creates a space-time cube appearance (d). Individual timeslices can be removed for inspection and global overlays show the

focus nodes’ movements over time. For illustration purposes and simplicity, each timeslice subgraph shares the same layout.

In practice, each timeslice subgraph is laid out semi-independently of the others, resulting in node movement between time

points.

approach to computing the layout of the dynamic net-

work: for each timeslice t, we utilize the layout of

the previous timeslice t − 1 as an initial layout before

commencing the layout process. This ensures that the

nodes’ movement over time will remain consistent

(i.e no ﬂickering or popup effects) while preserving

the viewer’s mental map in the transitions from times-

lice to timeslice (Archambault and Purchase, 2013).

Here, the movement of the nodes between times-

lices depicts their trajectories over time as edges form

or dissolve, pulling or pushing the nodes, respectively,

reﬂecting the network’s evolving structure. In con-

trast, an aggregation approach, i.e. computing sin-

gular positions for each node based on the layout of

the super graph (Figure 2a), would result in no node

movement over time. Figure 3a shows an example

of the layouts of multiple timeslices overlaid on top

of each other and highlighting nodes’ movement over

time (orange lines), making it difﬁcult to make sense

of changes over time. Instead, the focus nodes’ tra-

jectories, i.e. their movement between timeslices, can

be better perceived in Figure 3c.

Physical Embedding. Figure 1 shows our version

of a slide holder, used to stack the individual times-

lices printed on transparent media. To make sure

that our method supports a wide variety of fabrica-

tion techniques, we add manufacturing parameters

to the physical embeddings. The physical representa-

tion depends on the transparency and format of the

slides used. Additionally, the desired colors can be

chosen depending on the availability of color print-

ing.

First, we transform the node positions of the indi-

vidual timeslices for the physical embedding to the

format of the desired output. This corresponds to

the paper size, with additional margins that ensure

that the slide holders do not obscure any data. For

all timeslices, we calculate the minimum and maxi-

mum x and y positions of the nodes. We then map

the values to the space between the chosen margins.

We then create separate embeddings for the chosen

focus and context subgraphs. To emphasize nodes

of interest, the focused subgraph is embedded using

distinct node colors. In the context subgraph, we use

smaller node diameters and a lighter hue of gray to

avoid visual obstruction in the HoloGraph. The edge

weights are encoded into edge thickness in both sub-

graphs. Figure 3b shows both of these embeddings

superimposed. Printing the focus and context embed-

dings separately allows users to customize the rep-

resentation interactively. Figure 4a shows the inser-

tion or removal of a context slice from a HoloGraph.

Finally, we also create global overlays to trace the

movement of focus nodes over time, shown in Fig-

ure 3c. For this, we draw a polyline for each focus

node through all its positions in the timeslices. At the

node positions, we add a bright circle to emphasize

where the node can be found. Figure 4b shows the

process of adding this slice to the HoloGraph. We

create a separate overlay for node labels. This way

the labels can be removed freely, minimizing possible

obstructions as shown in Figure 4c.

4 CASE STUDY

To examine the potential of HoloGraphs, we present

a case study highlighting social network interactions

between characters in a famous children’s book se-

ries.

IVAPP 2025 - 16th International Conference on Information Visualization Theory and Applications

862

(a) Individual timeslices overlaid. (b) Focus + Context (c) Global overlays

Figure 3: Embeddings for individual timeslices, Focus + Context slices as well as Labels + Trajectories overlays. (a) The

embedding of the subsequent slice is dependent on the previous. Disappearing (1) and appearing (2) links and the subsequent

re-embedding causes movement of the nodes (3). (b) shows a superimposition of the focus subgraph, indicated by larger and

colored nodes, and the context subgraph with smaller, faint grey nodes for a single timeslice. (c) shows the global overlays

for focus node trajectories and labels.

Data. We use a dynamic network representing the

character interactions in the series of Harry Potter

books by J.K. Rowling

to show the capabilities of

our approach. The dataset is processed as a time-

sliced network, where the nodes represent the char-

acters in the books, and the weighted edges represent

the number of interactions between those characters

within each book. The individual timeslices represent

the state of the character interaction graph within each

book. We reduced the size of the original dataset to vi-

sualize the most central and inﬂuential actors as well

as the most important relationships. By ﬁltering out

the links whose weights were not in the top 10 per-

centile we additionally reduce clutter and put an em-

phasis on the more important relationships between

the characters of the data. This process resulted in a

set of 111 nodes (characters) and 612 weighted edges

(relationships) over 7 timeslices (books). From these

110 nodes, ten focus nodes, i.e. “egos” (Ehlers et al.,

2024b), were selected (Figure 3c).

Implementation. Our pipeline’s virtual embedding

was done using d3.js (Bostock et al., 2011) and its

physical embedding using qt/python. For the focus

nodes, we use the top 10 nodes with the highest de-

gree centrality, present in each of the seven timeslices.

We used laser-printer-compatible overhead slides as

the medium for the slices. The slide holder and base

were 3D printed in a Prusa MK2S. To use the entire

printer surface, we decided to print the slices on half

of an A4 page, resulting in 2 slices per page. The

slices were attached to the holders using a hole punch

on both sides of each slide to keep them in place. The

https://github.com/nikhil-ravi/

harry-potter-interactions

slice holders ﬁt into the left and right sides of the base,

ensuring slices remained parallel when mounted. Fig-

ure 1 shows a fully assembled HoloGraph, Figure 4

shows how different overlays are added or removed.

Findings. Using our Harry Potter HoloGraph, we

present some notable ﬁndings that can be extracted

from the time-sliced dynamic network. Our partic-

ipant had an interest in the Harry Potter series as

well as basic knowledge of node-link diagrams. They

were, however, unfamiliar with the concept of dy-

namic graphs. We then asked the participant to ex-

plore our HoloGraph, discussing notable events, char-

acter moments, and group interactions. We present

the insights, ﬁndings, and emphasize the reasoning

and suggestions we obtained with the goal of im-

proving our approach in communicating dynamic net-

works to non-expert audiences. The aim of our work

is to provide affordable data physicalizations to in-

crease knowledge and engagement using a hands-on

approach and interesting datasets.

Harry Potter, as the protagonist of all the books,

takes a stable position in the center of the graph.

His movements are minimal, however, we can ob-

serve how other nodes of interest behave in relation

to him. Rubeus Hagrid, for example, is a central char-

acter in the ﬁrst books, often interacting with Harry

as his “window to the wizard world”, however, in

later books, he leaves the school and has other in-

teractions. While he remains tethered to Harry and

in a stable orbit around him, his other interactions

cause him to move away from the central set of char-

acters. Albus Dumbledore’s interactions with Harry

are often distant and occur at the end of the individ-

ual books. In books ﬁve and six, he becomes closer

associated with Harry, and they form a personal rela-

HoloGraphs: An Interactive Physicalization for Dynamic Graphs

863

(a) Context slices (b) Trajectory overlay (c) Labels

Figure 4: Composition of a HoloGraph. Focus slices show the nodes of interest at every timeslice. Context slices can be

added to each focus slice individually (a). Node trajectories of focus nodes can be added as a global overlay (b), together with

focus node labels (c).

tionship, culminating in private tuition sessions. After

his departure from the active cast in the sixth book,

his node moves towards new characters related to his

backstory and away from the central set of charac-

ters. Fred and George Weasley are twins and are of-

ten shown in interactions together. This is supported

by their very similar, sometimes parallel trajectories.

They become separated close to the end of the last

book when Fred’s trajectory is a little shorter than

Georges’s. Finally, one of the most faceted charac-

ters, Severus Snape also has an interesting trajectory.

He spends most of the time in a stable position, close

to the center, as part of the “Hogwarts cluster’’. He

also joins another faction dedicated to the protection

of Harry in the later books, leading to continued prox-

imity. Finally, he changes factions again when his

node is pulled away from the other teachers in the

school and towards the antagonists. Issues The node

trajectories often point towards shifting associations

of the characters with different groups. However, in

the presented version of the Harry Potter HoloGraph,

these groups are not highlighted. Our expert recom-

mended visualizing these groups by highlighting per-

taining nodes in different colors in the context view.

Node trajectories could be easier explained by having

this added context. We also omitted different char-

acters in favor of readability. Our selection of nodes

of interest was purely based on node centrality. A

suggestion here was to emphasize a different selec-

tion of characters that interact in different ways with

the central characters. Notably, Voldemort, the pri-

mary antagonist of the story was not included in the

focus nodes for lack of centrality. Other nodes, such

as the love interests of different characters could have

interesting trajectories in conjunction with the associ-

ated characters. Finally, while the trajectory overlay

was overall helpful in ﬁnding nodes of interest in the

different timeslices, it led to some confusion. Our ex-

pert thought that a certain move of the node occurred

early on in the data, but noticed that the node was in a

different place than expected at that time. The partici-

pant suggested indicating a direction in the trajectory

lines, for example by narrowing the lines from ﬁrst to

last timeslice.

5 CONCLUSIONS

Takeaways. Our work presents an accessible and

affordable way to create interactive physicalizations

of dynamic graphs. Our overarching goal is for this

approach to be applied to improve visualization liter-

acy and provide an engaging method for communi-

cating science-related concepts in education settings.

In our work, we investigated small to medium-sized

graphs using the Harry Potter dataset. However, the

number of nodes that can be displayed is limited only

by printer resolution and the size of the used medium.

Because we are printing our embeddings with 2D

printing devices, the number of nodes can be much

larger than in 3D printed graphs, where maximum

node numbers of around 30 nodes are common (Bae

et al., 2024; Drogemuller et al., 2021). Our ﬂexible

approach allows for the use of different sets of fo-

cus nodes as well, so that different aspects of the data

can be examined with minimal reassembly, where, for

3D printed representations, printing times are often a

limiting factor (Drogemuller et al., 2021). Most of a

HoloGraph only has to be built once, and the various

slices of a dynamic graph can be printed quickly and

inexpensively. Compared to the approach of Bae et

al.(Bae et al., 2024), HoloGraphs provides interactiv-

ity without the use of electronics or other augmenta-

IVAPP 2025 - 16th International Conference on Information Visualization Theory and Applications

864

tion. The case study conﬁrms that our HoloGraphs

can convey several facts about dynamic graph visu-

alization at the hand of a simple example. A per-

son with knowledge about the underlying data was

able to quickly reason about important story events

and character moments regarding several nodes of in-

terest. Thus, using a dataset already familiar to the

user, made it possible to explain the mechanisms of

dynamic networks to a person with no prior knowl-

edge of or experience with dynamic graphs. Finally,

our domain expert also pointed out interesting aspects

our representation could display: highlighting differ-

ent groups in context slides could explain node move-

ment in conjunction with group association, while the

trajectory overlay could be easily adjusted to help ﬁnd

nodes at different time points.

Limitations. The number of timeslices that a Holo-

Graph can display depends on several material fac-

tors. With the overhead projector slides we used, sig-

niﬁcant obstructions became apparent using about 20

slices (10 focus + 10 context slices). We used A5

pages (A4 halved) in landscape format for our em-

beddings. With a standard printer, sizes of up to an

A4 page per timeslice are possible. Framing has to

support larger slice formats well enough for the slices

to remain parallel. Drogemuller et al. (Drogemuller

et al., 2021) show that users prefer haptic feedback in

physical networks. Our approach, however, sacriﬁces

the tactile component, in favor of visual clarity.

Future Work. In this work, we investigated times-

liced networks, however, many real-world networks

are not discrete. In future work, we aim to investigate

the use of HoloGraphs for continuous network physi-

calization. Our evaluation presents some insights into

how our method can help with understanding the un-

derlying data, visualization techniques, and what in-

sights can be extracted from dynamic network struc-

tures and behaviors. However, the versatility and ac-

cessibility of our method could support a multitude of

dynamic data.

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