=Paper=
{{Paper
|id=Vol-2744/paper9
|storemode=property
|title=Graph-Based Visual Analytics Tools for Digital Humanities Research
|pdfUrl=https://ceur-ws.org/Vol-2744/paper9.pdf
|volume=Vol-2744
|authors=Konstantin Ryabinin,Konstantin Belousov,Svetlana Chuprina
}}
==Graph-Based Visual Analytics Tools for Digital Humanities Research==
https://ceur-ws.org/Vol-2744/paper9.pdf
Graph-Based Visual Analytics Tools for Digital
Humanities Research ?
Konstantin Ryabinin1[0000−0002−8353−7641] ,
Konstantin Belousov1[0000−0003−4447−1288] , and
Svetlana Chuprina1[0000−0002−2103−3771]
Perm State University, Bukireva Str. 15, 614990, Perm, Russia
kostya.ryabinin@gmail.com, belousovki@gmail.com,
chuprinas@inbox.ru
Abstract. This paper is devoted to the development of the Web application for
the visual analytics of the interconnected data within digital humanities research
highly adaptable to the specifics of application domain and personal analytics
preferences. The circular graph is proposed as a visual model to depict the in-
terconnected data in a comprehensive way. The graph rendering software is or-
ganized according to the model-driven architecture utilizing ontology engineer-
ing methods and means, which ensure configuration flexibility and modification
ease. The functioning scenarios of the application’s visualization component can
be changed without its source code modifications, just by editing the underlying
ontology that describes data processing and rendering mechanisms. Extraction,
transformation, loading and rendering of the data are configured in the intuitive
way by data flow diagrams with the help of a high-level graphical editor. The
described features are demonstrated on the real-world examples from the digital
humanities application domain.
Keywords: Visual Analytics, Circular Graph, Data Filtering, Data Comparison,
Ontology Engineering, Digital Humanities.
1 Introduction
Many tasks in digital humanities (DH) research involve the processing of the linked
data, wherein the graph theory appears to be a powerful methodological and technolog-
ical base for solving associated problems. Taking into account the specifics of DH, the
considered data are normally quite big, but their handling requires human attendance
and cannot be fully automated. One of the key means to help DH specialists to fulfill
their everyday work is scientific visualization and visual analytics (VA) that allows to
present related data in an observable interpretation-ready form. Our goal is to develop
an ergonomic and flexible tool for graph-based visualization of interconnected data
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons Li-
cense Attribution 4.0 International (CC BY 4.0).
?
The reported study is supported by Ministry of Science and Higher Education of the Russian
Federation, State Assignment No. FSNF-2020-0023.
2 K. Ryabinin et al.
that allows comprehensive VA within DH research. The new high-level component for
circular graphs’ visualization is presented to tackle data filtering problems and improve
the cognitive power of visual analytics. Ontology-driven data extraction, transformation
and loading (ETL) mechanism is proposed to enable obtaining the data from different
sources and process them in a flexible way. The software developed is demonstrated by
solving the problems from applied linguistics domain.
2 Background and Related Work
VA is no doubt a powerful methodology to conduct research in a field of DH, but, as
indicated in [9], nowadays there is a noticeable talent gap between the VA scholars and
digital humanists. While DH and VA have a huge potential of coevolution, the research
results presented in the literature are typically valuable either only for DH, or only for
VA, and rarely for both simultaneously [9]. This is because DH projects often lack
researchers with deep computer science skills, and consequently have to rely on the ex-
isting general-purpose visualization tools, instead of driving the actual software devel-
opment. But in that case, some tasks remain unsolved because of traditional software
limitations [8,9]. W. Huang et al. tackle this problem by proposing a so-called user-
centered approach to the process of visualization making (graph-based visualization in
particular). This approach ensures the creation of cognitive graphics tools, which devel-
opment comprises design and evaluation stages [8]. On the design stage, “the designer
applies design principles and chooses the visualization best supporting perception and
cognition”, and on the evaluation stage “visualization is evaluated to understand how
cognitive processes are affected” [8].
Similar, but slightly less formal approach is proposed by S. Jänicke, who describes
an “ideal” VA+DH project as a close collaboration between the computer scientists
and digital humanists, where each visualization feature proposed is immediately tested
and validated in terms of its viability for DH research and then either approved for
further development or rejected [9]. Working on our VA tools, we have chosen this
exact strategy.
For graph visualization, the Gephi system is traditionally used [7]. Being feature-
rich, this system, however, provides instruments for layout the graphs of free structure,
while we found out, that sometimes the circular graphs [2] are more comprehensive by
depicting data sets. Moreover, as stated in [15], it is often desirable to have the graph
visualization tools in a Web application, without installing additional software.
An important point of graph visualization is the data preparation stage. To ensure
the intuitive and flexible data preparation process we suggest to declare its steps by
data flow diagrams (DFDs) [10]. A lot of popular visualization software use such an
approach, for example, Blender, Maya, Substance Designer, etc., so it proved its effi-
ciency in terms of data processing and rendering pipeline declaration.
We use model-driven architecture based on the ontology engineering methods [6]
to achieve the configuration flexibility and adaptation of the software to the specifics of
the application domain without source code modification. We construct the ontologies
within visual editor ONTOLIS [6].
Graph-Based Visual Analytics in Digital Humanities... 3
Our previous research work was dedicated to the development of ontology-driven
scientific visualization and VA system called SciVi1 [13]. This system is portable across
all the popular platforms (Windows, GNU/Linux, macOS, iOS, Android). It is orga-
nized as a client-server application, having both thick (native, written in C++ using Qt
5 framework) and thin (browser-based, written in TypeScript and JavaScript, utilizing
HTML5 and CSS3) clients. The behavior of this system is fully controlled by underly-
ing ontologies, which allow deep reconfigurations of SciVi, extension of its ETL and
data rendering capabilities, whereby leveraging adaptation to the completely new vi-
sualization and analytics tasks without changing the source code of its core. Faced the
problems in a field of DH during the case study of verbal and nonverbal behavior of so-
cial network users, we built the graph VA toolset upon the SciVi [12]. Tried out different
graph layouts, we focused on the circular one because of its good readability [2].
We implemented a graph visualization SciVi component (called SciVi::CGraph) as
a Web application in TypeScript utilizing PixiJS2 rendering engine. The graph nodes
are uniformly distributed on a circle and the edges are drawn as quadratic Bézier curves
with the control point in the circle’s center. Different slices of data can be displayed on
the same graph using a scale of states that allows fast switching between them. Data
slices can be organized in a hierarchy, therefore this scale supports multiple levels.
Examples of different graphs can be found online: https://graph.semograph.
org/cgraph/.
SciVi tools have been integrated into Semograph information system [4]. Semo-
graph is aimed to solve different DH tasks involving methods of computational linguis-
tics by supporting a wide range of operations on the textual content, including tagging,
classification of terms, building semantic relations, etc. The integration with SciVi al-
lowed to utilize advanced visualization features including the rendering of graphs.
3 ETL Mechanism
The conceptual scheme of the data processing within the SciVi system is shown in
Fig. 1.
Currently, CSV format is used to transfer data from Semograph into SciVi, since
export to this format is natively supported by Semograph. However, it is easy to switch
to any other data representation since the ETL mechanism of SciVi is very flexible. This
mechanism is implemented within the SciVi Data Processing Module and driven by the
ontological knowledge base. Underlying ontologies describe different data formats and
data interpretation rules, as well as available data preprocessing filters and data visu-
alization techniques. Thanks to this, changing or extending these ontologies is enough
to alter SciVi behavior adapting it to the new VA tasks. But the changing of ontolo-
gies requires knowledge engineering skills, thereby is unwanted for the end-users and
is dedicated to the system administrator.
The end-users are provided with a more high-level steering instrument: Data Flow
Editor. This SciVi module (based on the Rete3 JavaScript framework) implements a
1
https://scivi.tools
2
https://www.pixijs.com
3
https://rete.js.org/
4 K. Ryabinin et al.
Fig. 1. Data processing pipeline within SciVi.
graphical user interface (GUI) to compose a data processing algorithm from the high-
level building blocks utilizing DFDs. The example of DFD describing the extraction of
data from an arbitrary CSV file is shown in Fig. 2.
Fig. 2. DFD defining ETL and data visualization in SciVi.
Each node in the DFD represents a particular step in data obtaining, processing or
visualization. For example, “CSV Table” defines file reading; “Get Range” allows to
specify the subset of values within the CSV table; “Make Nodes” sets up the composi-
tion of the graph nodes internal representation; “Make Graph Data” corresponds to the
stage of interconnecting the graph nodes with weighted edges; “Circular Graph” defines
the data rendering using SciVi::CGraph visualization component. Links between DFD
nodes depict the data flow and their color is bound to the type of transmitting data.
The set of available DFD nodes’ types correspond to the set of operations on the data
available in SciVi. It is constructed automatically according to the underlying ontology
and presented to the user as a toolbar palette. Each data processing operation has its
Graph-Based Visual Analytics in Digital Humanities... 5
own description that may be altered or extended to change the actual behavior of the
entire system. For example, the ontology fragment describing “CSV Table” is shown in
Fig. 3.
Fig. 3. Fragment of SciVi ontology describing CSV table reader.
It can be seen, that “CSV Table” node is treated as a data source, has CSV file
as a setting parameter and table of values as an output. The actual implementation of
this data reader is described by the “CSV Table Worker” concept in the ontology. This
concept has an internal attribute (not drawn in the figure, since the figure shows concepts
and relations only) with a link to the JavaScript code fragment that implements CSV
reading with help of PapaParse4 library. An important part of “CSV Table” description
is “ClientSideWorker” concept. It identifies that the reading and parsing takes place
within the browser (on the client side), without sending the data to the SciVi server.
Although the SciVi architecture allows server-side processing, currently the amounts of
data we faced in our tasks were small enough to be handled locally.
4 Visual Analytics Component
SciVi::CGraph VA component accepts the data in JSON representation. Once the user
has created the DFD for the particular task and started the visualization, this component
renders the graph and provides its own GUI allowing different interactions with that
graph, including zooming, panning, nodes and edges selection, data filtering, etc. The
most important distinctive features of SciVi::CGraph are described below.
4.1 Multilevel Ring Scale
In case, when a classification of graph nodes is defined, SciVi::CGraph draws a special
ring scale around the graph to visually highlight the given nodes’ classes. The number
4
https://www.papaparse.com
6 K. Ryabinin et al.
of rings in this scale is potentially unlimited, so the nodes’ classifier can have multiple
levels. A special tree view in a sidebar of the graph allows to explore the classifier and
switch the visibility of nodes belonging to individual classes. Colors of the ring sectors,
which depict the classes, can be assigned manually, but also set automatically based
on the special heuristic algorithm that maps the classifier’s hierarchy to the HSV color
model in a way the neighbor ring sectors have distant colors to be visually distinguish-
able.
To evaluate different hypotheses, the user can change the order of scale rings by
drag and drop, command the graph to sort the nodes accordingly and set the color of
nodes to the color of any ring sector they belong to. These interactions help to find out,
which order of hierarchy levels is the most meaningful one in terms of structuring the
interconnected data.
Fig. 4 shows5 the results of the correlation analysis of 38 topics extracted from 48
stories told by informants as self-presentation [11]. The sample of informants is bal-
anced by sex, age, and education level. Graph nodes depict self-presentation texts, edge
thickness represent correlations coefficients (all correlations are positive; all coefficients
below 0.8 are filtered out). Social (education level: secondary, higher) and demographic
(sex, age group) parameters are shown on the ring scale groping the nodes accordingly.
The groups are nested according to the order of the rings.
a b c
Fig. 4. Correlation of topics in self-presentations, grouped by age (a), education (b), and sex (c).
The aim is to find out, which parameter dominates by grouping the informants to-
gether. Related to DH it means to find, which social / demographic informant groups
consolidate more by talking about themselves. Related to the graph theory it means to
find, which layout of nodes provides their better clustering. The proposed mechanism of
the ring scale reordering allows quick checking of different variants and inspecting them
visually. While Fig. 4a (topmost grouping by age) and 4b (topmost grouping by edu-
cation level) look messy, Fig. 4c reveals significant dense “community”, corresponding
to the stories told by females (at the same time, there is almost no correlation between
5
The interactive graph is available online: https://graph.semograph.org/cgraph/
aboutmyself/index.html
Graph-Based Visual Analytics in Digital Humanities... 7
males’ stories). Further interpretation of this material is outside of this paper’s scope,
but the corresponding milestone of related DH research is considered to be reached. It
is worth noting, that it took less than a minute to find this solution using SciVi::CGraph.
4.2 Equalizing Filter
Sometimes the noisy data on a single graph may have a non-uniform distribution of the
noise strength. In this case, filtering the entire data set with the single threshold appears
to be meaningless and threshold adaptivity is required. We often face this problem in
multipartite graphs comprising interconnected data of different nature, or data, which
parts were differently preprocessed. To tackle this problem, we propose a so-called
equalizing filter that can have individual parameters for selected groups of nodes and
edges (resembling the sound equalizer that can differently affect selected parts of the
spectrum).
Currently, the equalizing filter within SciVi::CGraph operates as a set of range-
based cutoff functions tied to the ring scale. By default, there is one global cutoff func-
tion (affecting the entire graph), but, if needed, the user can add auxiliary local ones for
any sector of the ring scale. If a node or an edge is affected by multiple cutoff func-
tions (global one and multiple local ones according to the hierarchy of the ring scale),
their ranges are intersected to build the resulting filter. Node or edge is filtered out if its
weight lays outside the functions’ range intersection.
The practical use case of the equalizing filter is demonstrated in Fig. 5.
Fig. 5. Relationships between the verbal behavior of social network users and their psychological
characteristics.
8 K. Ryabinin et al.
This figure represents6 the filtered data of the relationships between the verbal be-
havior of social network users (SNUs) and their psychological characteristics. The psy-
chological parameters are obtained by two questionaries (personality features and self-
esteem) [14] fulfilled by the sample of SNUs. The verbal behavior is revealed with
the help of the linguistic analysis from the comments written by these users in social
networks. The filtering is individual for each psychological parameter because each of
them has its own statistical features (minimum, maximum, average, standard deviation).
This approach allows leaving only the dominant indicators for each psychological pa-
rameter. Fig. 5 demonstrates, that after equalizing the indicators, it can be revealed that
the SNUs of the female gender, who use obscene words in the public social network
space, are characterized by low self-esteem, low conscientiousness, low agreeableness,
high neuroticism, middle openness, and low extraversion.
4.3 Graph State Calculator
To visually compare the structure of data slices displayed in the graph, we implemented
a special graph state calculator. It allows to perform a sequence of basic set operations
on the graph states: union, intersection, difference, and symmetric difference.
Fig. 6 demonstrates7 the states of “Moscow” geoconcept. In this research, under
the term “geoconcept” we understand a set of collective opinions about a geographical
object. These opinions can be revealed from the associations people come up with [16].
Graph nodes represent the semantic categories of associations (extracted according
to the special classifier within Semograph system), edges identify the co-presence of
linked categories in the analyzed associates (derived from a group of informants). The
actual structure of geoconcept presented as a set of association categories depends on
the region. In this experiment we collected 3 datasets: in Perm (Fig. 6a), Biysk (Fig. 6b)
and Orenburg. The state scale (drawn below the graph) provides quick navigation be-
tween these data sets and makes it possible to visually compare them. However, to make
this comparison more elaborated and meaningful, set operations can help. As an exam-
ple, Fig. 6c shows the intersection of Perm and Biysk data sets, allowing to view their
common parts.
Thus, the graph state calculator provides a good basis for conducting comparative
DH studies and facilitates the process of interpreting research results.
5 Conclusion
Thanks to the features discussed, SciVi::CGraph allows advanced interactive VA of
interconnected data in DH. According to the feedback from the DH researchers of
Perm State University, this tool outperforms traditional graph analysis software like
Gephi in the tasks, which require special analytics features. Like SciVi VA system,
6
The interactive graph is available online: https://graph.semograph.org/cgraph/
psycho_reduced/index.html
7
The interactive graph is available online: https://graph.semograph.org/cgraph/
geoconcepts_reduced/index.html
Graph-Based Visual Analytics in Digital Humanities... 9
a b c
Fig. 6. States of “Moscow” geoconcept as viewed in Perm (a) and Biysk (b) along with their
intersection (c).
SciVi::CGraph is an open-source project licensed under the terms of GPLv3: https:
//github.com/scivi-tools/scivi.graph.
SciVi::CGraph is being iteratively developed in close collaboration with DH spe-
cialists and each new feature is immediately evaluated in real-world research projects
(in exact accordance with the cooperation model described in [9]).
For example, SciVi::CGraph was used by exploring the egocentric field of speaker
in the macedonian language [5], in the study of social network users’ speech within the
research project of Perm State University supported by Ministry of Education and Sci-
ence of the Russian Federation, state assignment No.34.1505.2017/4.6 [3], and by the
semiotic analysis of geomental maps [16]. Also, SciVi::CGraph was utilized in the Sir-
ius education center within the project “Images of Large Russian Cities in the Linguistic
Consciousness of Senior Schoolchildren” [1].
Taking into account the needs of conducted DH research, we plan to extend our
scientific visualization system SciVi with new feature-rich visualization components
for free structure graphs, graphs with volumetric 3D layout and graphs pinned to geo-
graphic maps.
We would like to thank Alexey Gorodilov, Elena Erofeeva and Ekaterina Khudyakova
for fruitful discussions on the papers topic.
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