=Paper=
{{Paper
|id=Vol-3286/09_paper
|storemode=property
|title=Bridging Representation and Visualization in Prosopographic Research: A Case Study
|pdfUrl=https://ceur-ws.org/Vol-3286/09_paper.pdf
|volume=Vol-3286
|authors=Alessandra Urbinati,Enrico Burdisso,Arthur Thomas Edward Capozzi Lupi,Claudio Mattutino,Salvatore Vilella,Alfonso Semeraro,Giancarlo Ruffo,Carlo Corti,Stefano De Martino,Elena Devecchi,Erica Scarpa,Giulia Torri,Rossana Damiano
|dblpUrl=https://dblp.org/rec/conf/aiia/UrbinatiBLMVSRC22
}}
==Bridging Representation and Visualization in Prosopographic Research: A Case Study==
https://ceur-ws.org/Vol-3286/09_paper.pdf
Bridging Representation and Visualization in
Prosopographic Research: A Case Study
Alessandra Urbinati 1,∗ , Enrico Burdisso1 , Arthur Thomas Edward Capozzi Lupi1 ,
Claudio Mattutino1 , Salvatore Vilella1 , Alfonso Semeraro1 , Giancarlo Ruffo4 ,
Carlo Corti3 , Stefano De Martino2 , Elena Devecchi2 , Erica Scarpa2 , Giulia Torri3 and
Rossana Damiano1,∗
1
University of Turin, Italy, Dipartimento di Informatica
2
University of Turin, Italy, Dipartimento di Studi Storici
3
University of Florence, Italy, Dipartimento di Storia, Archeologia, Geografia, Arte e Spettacolo
4
University of Eastern Piedemont ”A. Avogadro”, Dipartimento di Scienze ed Innovazione Tecnologica
Abstract
In the last decade, the research on ancient civilizations has started to rely more and more on data sci-
ence to extract knowledge on ancient societies from the written sources delivered from the past. In this
paper, we combine two well-established frameworks: Linked Data to obtain a rich data structure, and
Network Science to explore different research questions regarding the structure and the evolution of an-
cient societies. We propose a multi-disciplinary pipeline where, starting from a semantically annotated
prosopographic archive, a research question is translated into a query on the archive and the obtained
dataset is the input to the network model. We applied this pipeline to different archives, a Hittite and a
Kassite collection of cuneiform tablets. Finally, network visualization is presented as a powerful tool to
highlight both the data structure and the social network analysis results.
Keywords
Knowledge Graphs, Social Network Analysis, Digital Humanities, Network Science
1. Introduction
In the last decade, the research on ancient civilizations has started more on more to rely on data
science to investigate social and cultural aspects of these civilizations from the written sources
delivered from the past. In particular, a line of research has addressed the use of social network
analysis (SNA) for studying the social and political structures of the past by leveraging the
mention of entities such as locations and personages in texts. The advancements in network
analysis for historical research have paved the way to novel methods for investigating the
structure of communities from texts and their relationship with personal and geographical
data, with proof of concepts ranging in time and space [1, 2, 3, 4, 5].
In parallel with this trend, the advent of Linked Data has made semantic resources available
for archaeological and historical research, with notable examples such as CRM Archaeo [6] and
1st Italian Workshop on Artificial Intelligence for Cultural Heritage (AI4CH22), co-located with the 21st International
Conference of the Italian Association for Artificial Intelligence (AIxIA 2022). 28 November 2022, Udine, Italy.
∗
Corresponding authors.
£ alessandra.urbinati@unito.it (A. U. ); rossana.damiano@unito.it (R. Damiano)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
FPO [7]. Today, the use of semantic representation techniques and SNA methods can provide
an integrated approach which combines the shared, unambiguous definition of entities and
relationships in the historical domain with the tools and methods made available by SNA.
Thanks to the visualization of the social networks created from the semantically annotated
historical data, the implicit social and political structures of the past can be appraised in a
more insightful way by the domain experts.
In this paper, we describe a multi-disciplinary pipeline where, starting from a semantically
annotated prosopographic archive, a research question is translated into a query to the archive,
and the obtained dataset is the input to a SNA component, which outputs a graph and the
corresponding visualization. The pipeline is being developed as part of a research project enti-
tled “Networks of Power: Institutional Hierarchies and State Management in Late Bronze Age
Western Asia (NePo)”, funded by the Italian Ministry of Research, which concerns the study
of networks operating at local and regional levels in the Mesopotamian-Anatolian region. At
the this stage of the project, the complete, yet not fully automated pipeline has been created
and tested on the prosopographic data gathered so far from two corpora of cuneiform texts:
the Hittite archive including data on 353 personages, and Kassite archive including data on 236
personages.
The paper is structured as follows. In Section 2, we introduce the domain and survey the
applications of network analysis to historical research. In Section 3, the representation of proso-
pograhic data and the creation of the datasets; Section 4 describes in more detail the network
analysis tasks. In Section 5, network analysis and visualization is described through a set of
case studies. Conclusion and future work end the paper.
2. Ancient Near-Eastern Prosopography
The NePo project aims at a detailed analysis of Late Bronze Age (LBA) court structures, their
internal networks, and the economic systems they were controlling, on the basis of selected
epigraphic and archaeological sources, with the ultimate goal of obtaining a comparative pic-
ture of the royal elites of LBA Western Asia through the analysis of multiple sources from the
geographical areas under consideration.
Late Bronze Age documents dealing with the administration and economy of the Near East-
ern kingdoms and polities differ as regards their typology, contents, and aims. The corpus
of the Kassite cuneiform tablets includes royal inscriptions, internal and international corre-
spondence, and legal and administrative documents. Although Kassite material mostly comes
from the city of Nippur, the project case study focuses on a corpus of ca. 800 Kassite-period
cuneiform tablets published by [8] and dating to the 14th-13th centuries BC and whose prove-
nance is not certain. These documents currently represent the second largest, internally coher-
ent set of Kassite-period sources after that of Nippur. They are administrative records mainly
dealing with the income, storage, and redistribution of agricultural products (mostly cereals,
but also sesame, pulses, and cress) and by-products (beer and flour), animal husbandry, and
textile production; smaller groups of texts include legal documents and letters.
The Hittite documentation includes very few administrative and economic records [9] but is
very informative as far as the governance of the state and the role played by the court and the
officials are concerned. Differently from Mesopotamia, in fact, where very large archives of
administrative and economic records have been discovered and studied, little is known about
administration in Hittite Society. The corpus of Hittite written documents mainly consists
of the tablets and sealings found at Hattusa and other Anatolian sites such as Maşat Höyük/-
Tapikka, Kuşaklı Höyük /Sarissa, Kayalıpinar/Samuha, and Ortaköy/Sapinuwa. The research
takes also into consideration the documents from the Syrian polities subordinated to Hatti,
namely, Karkemish, Ugarit, Alalah, and Emar. Hittite written documents and the sealings men-
tion a huge number of personal names, titles and professions that await to be fully studied.
In order to overcome the intrinsic limits posed by the fragmented nature of the evidence, the
project is characterised by an interdisciplinary approach that combines the traditional meth-
ods of philology, archaeology, prosopography and historical research with methods and tools
developed in the field of the digital humanities, such as: a factoid-based approach for develop-
ing consistent models that schematically describe the activities of a target group with direct
connections with the sources; social network analysis for studying and comparing the network
of relations through the ages and geographical places even in presence of datasets of different
size [1, 2]; data visualization tools that can enhance the interpretation of data by effectively
displaying the results of quantitative and quantitative queries.
In the state of the art, the use of SNA techniques in the study of cuneiform archives relies
on two main approaches, which consist, respectively, in using the social network to infer new
information about the nodes (e.g., merging nodes representing the same individual [10]), and
in inspecting the properties of the network on global scale (e.g., clustering nodes based on
their properties and relations [11, 1] to discover and confirm working hypothesis about social
structures). The NePo projects aims at overcoming the limitations pointed out for previous
approaches: in particular, the size of the available data, and lack of standardization of the
semantic labels employed for the annotation. The adoption of a semantic model for describing
the data partly overcomes these limitations, since it paves the way to the integration of new
data sets and the application of more advanced SNA techniques.
3. Representing Prosopographies
The representation of prosopography in the project relies on the pioneering work carried out
by Pasin and Bradley 2015. The Factoid-based Prosopographic Ontology1 revolves around the
notion of factoid, intended as a believed-to-be-true, reported event in some written source, a
definition that fits very precisely the data inferred from the corpora investigated by the project.
The Factoid model puts into play two basic entities connected with the notion of factoid: the
Source, where the factoid is asserted (a Hittite or Kassite cuneiform text), and the Relation it
describes (e.g., an administrative or legal relation), further specialized in a Person reference
and in a Location reference (respectively, the personages and places involved in the relation).
Starting from FPO, classes and properties have been specialized to adapt them to the Hittite
and Kassite domains (thus yielding the Hittite FPO, or HFPO, and the Kassite FPO, or KFPO),
by adding more specific classes and properties to the ones in the ontology, leaving the the core
model unaffected. In particular, Cuneiform texts and Seals have been introduced as specific
1
https://github.com/johnBradley501/FPO
Figure 1: Representation of a kinship relationship in the Kassite dataset.
source types; specific factoid types have been added to account for the information conveyed by
the cuneiform texts in the two domains, which concern Administrative, Kinship, Professional
and Legal relationships between the personages in the Hittite and Kassite world; finally, new
authoritative lists have been created to fit the representation of sources and personages in the
Hittite and Kassite worlds, where dates refer to sovereigns, and people are described according
to culture-specific terms for age and gender. Figure 1 illustrates an example factoid, namely
the kinship relationship between two personages sourced from a cuneiform text from Kassite
Babylonia.
The collection of Hittite person records is in the project carried out by using an installation
of the Omeka S platform, which allows collecting, publishing and sharing data with Linked
Open Data. The core of Omeka S is a relational database, but records can also be ingested
and accessed by third parties via an API in JSON-LD format (Fig. 2). The format of a record
consists of a template, which defines the record’s metadata scheme by combining the terms
from the RDF vocabularies uploaded to the platform; records can be interlinked when needed,
so as to represent straightforwardly the relations over classes encoded in the ontology. The
representation of factoids, personages and sources in the Hittite and Kassite domain, in fact,
relies on a set of vocabularies representing the main entities of the domain: besides the Factoid-
based Prosopographic Ontololgy (FPO) for the prosopographic domain (extended for the case of
Hittite and Kassite prosopography as sketched above), the Bibliographic Ontology vocabulary
(BIBO) for the relations with bibliographic sources, and the Dublin Core Metadata Element Set
(DC-MES) for the provenance and description of data.
4. Social Network Analysis
Network models conceptualize interactions between entities and allow to gain both a local and
a global view of the system. We define a network as 𝐺 = (𝑉 , 𝐸), where 𝑉 is a set of nodes,
and 𝐸 is the set of links that encode the relationship structure between nodes. This definition
can be enriched to improve the adherence of the model to reality. In particular, a network
Figure 2: The role of Omeka-S (center) in bridging the ingestion of data into Linked Data (right),
according to the Factoid-based model (left)
can be defined as directed, if the edges retain the information of direction; weighted, where
the function 𝜛 ∶ 𝑉 × 𝑉 → ℕ assigns for each pair of nodes 𝑖, 𝑗 ∈ 𝑉 the weight of edge (𝑖, 𝑗);
temporal, having specific representation for each timestamp 𝑇 . Moreover, a network model can
be dynamic [12], and can grow over time [13]. Another well established class is the bipartite
network whose nodes are divided into two sets, and only connections between two nodes in
different sets are allowed [14]. Since links often exhibit heterogeneous features, new structures
were theorized, with layers [15] or multiedges in addition to nodes and links [12]. In the more
general multilayer framework, a node 𝑖 in layer 𝑎 can be connected to any node 𝑗 in layer 𝑏.
Layers will represent aspects or features that characterize the nodes or the links that belong to
that layer. As a consequence, the set of links can be partitioned into “intra-layer links”, that is
links that connect nodes set in the same layer, and “inter-layer” which are those that connect
nodes set in different layers [16]. This flexibility allows applying the framework on multiple
tasks.
Information is rooted in the “connectedness structure” of the network and to exploit it re-
searchers must decide which resemblances, relationships between entities, or nodes encoding.
This relates to the kind of aspect of the data to be included as well as their relative impor-
tance. Different computational methods aim to preserve those properties the researchers want
to study and replicate.
Content exploration. Entity co-occurrence in text-analysis has been extensively explored
when dealing with ancient text, for example, Bornhofen et al. [17] employ a corpus of digitized
resources about European integration since 1945 to generate a dynamic multilayer network
that represents different kinds of named entities appearing and co-appearing in the collections.
Then they build a visualization tool that allows interest-driven navigation in the corpus and
the discovery of the interconnections of its entities across time [17]. Another fundamental
research question regards annotation comparison. Wieneke et al. [18] have developed a tool
to establish false negatives in the depiction of the same entities in the context of annotation
comparison.
Society structure. Social networks describe the connection of individuals. To better un-
derstand the hidden structure of these types of networks fundamental tasks are identifying
the most central nodes, and understanding the organization of nodes into groups. Centrality
measures and resulting node raking target the scope to establish the main player/actor in the
Figure 3: Excerpts of cuneiform texts referencing the same location (the town of Āl-irre)
network [19]. While clustering or community detection tackles the problem to establish parti-
tion of the network [20]. Studying social roles could also deeply impact the understanding of
how society and in particular ancient ones worked, and analyzing the interplay of political and
economic relationships can shed light on hierarchy and power dynamics. Breigher at al. [21]
famously set the basis for many other works coupling the marriage and the economic trading
between fifteenth-century Florence families.
Spatial network. Network model has been used to analyze hierarchical predominance in
cultural practices across different regions and the evolution of cultural trends. Mizoguchi et al.
[22] establish links between ten regional entities whenever the author found archaeologically
recognizable similarities in pottery styles and mortuary traditions. The work by Schich et al.
[23] aims to understand which processes shape and drive the geopolitical aspect of cultural his-
tory by a birth-to-death network, where nodes are countries and links represent the migration
of notable individuals over time from birth to death places. Spatial and social entities could
interact and discovering interesting motifs in the network could lead to new insights into the
life of ancient empires [24].
5. Bridging KG and SNA
The pipeline starts with the ingestion of the data into the Omeka S platform and ends with a
visualization of a network generated from the data to explore them or answer a specific research
question. The pipeline, which involves the roles of domain expert (historian and archaeologist),
knowledge representation engineer and data scientist, consists of the following steps:
1. Record creation: this step, carried out by a domain expert (namely, an archaeologist
or a historian), starts with the ingestion of the data extracted from the cuneiform texts
encoded in the clay tablets. This is where the interpretation of data first occurs, since ex-
perts are called to identifies the entities mentioned in the cuneiform text, often encoded
with different spellings, and the relations between them. An example is provided by Fig-
ure 3, which represents two cuneiform texts mentioning the same location in different
contexts. This step does not require any familiarity with the Linked Data formats, since
the ingestion is carried out via a set of interlinked web forms. Before the data gathering,
in fact, the interconnected record types representing the entities involved in the interpre-
tation of data (personages, locations, sources, etc.) have been created by the knowledge
engineer, in cooperation with the domain experts: for example, with reference to Fig. 1,
the Kinship Factoid record type (template in Omeka S terminology), the Person record
type and the Source record type are involved.
2. Triplification. In this phase, carried out by the knowledge engineer, the archive (or a
portion o it) is extracted in JSON-LD format from the platform via the Omeka S built-in
REST APIs2 and the resulting knowledge graph is stored into an Apache Fuseki3 triple
store. In practice, each record in the platform is mapped onto the corresponding entity
(e.g., factoid type, sources, person) in the ontology, and the entities are connected to each
other according to the ontology model; the record fields provide the triples describing the
entity. Notice that, differently from a relational scheme, this mapping is straightforward,
since records are directly encoded in the vocabulary provided by the prosopographic
ontology. For example, the kinship relation between two personages, a kinship factoid
(instance of the KinshipFactoid class), relates the two personages (instances of the Person
class) and the cuneiform text excerpt where their relationship is represented (an instance
of the Source class); additional triples – such as the specific kinship relation type, the
age and gender of the personages, or the reference location in the source text – are added
depending on the details available in the record.
3. Data extraction. Although in principle Omeka S allows formulating queries in a se-
mantic format by using the vocabularies employed to describe the data, in practice this
strategy falls short to translate the most complex research questions into queries on the
archive. To bypass this limitation, the research questions formulated by the domain ex-
perts (historian and archaeologist) are translated by the knowledge engineer into SPARQL
queries and executed on the knowledge graph. In order to reduce the interpretation gap
between the domain experts and the datasets, a form-based interface for preparing the
queries has been created. allows non-experts to query the graph by selecting the desired
characteristics of the personages and obtaining the corresponding SPARQL query (in the
figure, the personages related with the town of Nippur). Similar interfaces have been cre-
ated for the other relevant entities of the domains, such as the sources and the relations
between the personages.
4. Visualization. Finally, a network analysis algorithm is applied by the data scientist to
the data extracted from the dataset through the SPARQL query, and the obtained graph
is visualized and submitted to the domain expert for interpretation, with the goal of con-
firming or rejecting the assumptions behind the research question.
In the following, we describe three different research questions that have been explored by
employing the pipeline described above.
2
https://omeka.org/s/docs/developer/api/rest_api/
3
https://jena.apache.org/documentation/fuseki2/
R1. Investigating the co-occurrence between persons and locations sourced from the Kassite
document collection. The raw data, as shown in Figure 3, has been ingested into records and
stored in the knowledge graph. Through the query 5, we filter the information needed. For this
task, we defined a bipartite network 𝐺 = (𝑈 , 𝑉 , 𝐸), where 𝑈 is the set of Kassite persons, 𝑉 the
set of locations and 𝐸 the set of edges (𝑖, 𝑗) that exist between nodes 𝑖 ∈ 𝑈 and 𝑗 ∈ 𝑉 if the person
𝑖 appear in some activity in location 𝑗. The network is shown in Figure 4a. Further analysis
involves both the possible projections of the bipartite network. A projection is a compressed
version of the bipartite network that contains nodes of only either of the two sets, nodes are
connected only when they have at least one common neighboring in the other set. Figures 4b
and 4c show the projections obtained with simple weighting, that is where edges are weighted
by the number of times a common association between two nodes of the same set with the
same node of the other set is repeated. The projection networks can be leveraged to gain a
deeper view of the twofold system.
SELECT ? id ? name ? location
WHERE {
? person rdf : type kfpo : KassitePerson .
? person dcterms : title ? name .
? person omeka : id ? id .
OPTIONAL {? person kfpo : hasLocationName ? location }
}
R2. Investigating the geographical trade – in particular, how a specific administrative role
distributes over the cities of the Hittite Empire. We filter the data through the query 5: we
retain all the persons in the late Hittite period that appear as witnesses in some transactions.
We define the network to be undirected, weighted with colored node, 𝐺 = (𝑉 , 𝜛), where 𝑉 is
the set of Hittite persons and 𝜛 ∶ 𝑉 × 𝑉 → ℕ is the function defining for each pair of nodes
𝑖, 𝑗 ∈ 𝑉 the weight of edge (𝑖, 𝑗) that measures the times the nodes appear as witnesses in the
same trade. A network is said to be colored if we associate colors, or labels, to each of its vertices
and/or edges. We assign as labels the city to which the person belongs. Discovering motifs in
the resulting network would mean discovering interesting trade routes that were used in the
Empire.
SELECT ? name ? link ? to
WHERE {
? link dcterms : title ? name ;
hfpo : hasWitnessReference ? to .
? link hfpo : sourceLanguage ” Late Hittite ”.
}
R3. Understanding how different aspects of society relate to each other. To investigate the role
duality in the Kassite Empire, namely how kinship relations impact administrative relations,
after querying the data (a partial query is shown in Listing 5) we define a multilayer network,
where each node 𝑖 can belong to a different layer 𝛼. In this context, we define two layers, one
that encodes all the kinship relationships and one for the administrative relationship. Figure 6
shows the flattened version of the described network. Multilayer networks combine multiple
dynamics, that can be embedded both inside each layer and between them. To fully exploit
this type of structure, expertise in ancient cultures and societies must guide the final model
definition.
SELECT ? name ? type ? entities
WHERE {
? kin rdf : type kfpo : AdministrativeRelationshipFactoid .
? kin dcterms : title ? name .
? kin kfpo : administrativeRelationType ? type .
? kin kfpo : hasAdministrativeReference ? entities
}
(b) Projection over Kassite persons.
(a) Bipartite network. (c) Projection over Kassite Empire locations.
Figure 4: Kassite co-occurrence person-location bipartite network and projections. (b) represents the
network projection over the persons (|𝑈 | = 108, |𝐸| = 1475). (c) represents the network projection over
the locations (|𝑈 | = 20, |𝐸| = 51). Node dimensions scale over weighted degree.
6. Discussion and Conclusion
In this paper, we described a pipeline developed to support the study of ancient societies with
visualizations developed from a semantic representation of prosopographic data. Starting from
two related, yet distinct domains, we implemented the steps in the pipeline to test its feasibility
and methodological validity for the archaeologists and historians involved in the project. The
methodology and the obtained visualizations, designed by a multi-disciplinary team involving
knowledge representation experts, digital humanists, historians and visualization experts, have
been positively assessed by the domain experts, who have been able to confirm the research
hypotheses behind the creation of the networks.
As future work, we plan to fully automatize the pipeline, so that the extraction of data, the
construction of the network and its visualization can be executed in the same environment.
Figure 5: Late-period Hittite witness relationships. Nodes (|𝑈 | = 40) are Hittite persons; dimension
scales over the weighted degree; color schema is location. Edges (|𝐸| = 449) stand for two persons who
appeared as witnesses in the same transaction.
Figure 6: Kassite kinship and administrative relationships network. Nodes are Kassite persons (|𝑈 | =
208), their dimension scale over weighted degree. Edges (|𝐸| = 168) colors stand for relationship types.
References
[1] A. Wagner, Y. Levavi, S. Kedar, K. Abraham, Y. Cohen, R. Zadok, Quantitative social
network analysis (sna) and the study of cuneiform archives: A test-case based on the
murašû archive, Akkadica 134 (2013) 117–134.
[2] V. A. Traag, L. Waltman, N. J. Van Eck, From Louvain to Leiden: guaranteeing well-
connected communities, Scientific reports 9 (2019) 1–12.
[3] T. Alstola, S. Zaia, A. Sahala, H. Jauhiainen, S. Svärd, K. Lindén, Aššur and his friends: a
statistical analysis of neo-assyrian texts, Journal of Cuneiform Studies 71 (2019) 159–180.
[4] H. De Weerdt, B. Ho, A. Wagner, J. Qiao, M. Chu, Is there a faction in this list?, Journal
of Chinese History 中國歷史學刊 4 (2020) 347–389.
[5] E. Hyvönen, P. Leskinen, M. Tamper, H. Rantala, E. Ikkala, J. Tuominen, K. Keravuori,
Linked data: A paradigm shift for publishing and using biography collections on the se-
mantic web, in: Proceedings of the Third Conference on Biographical Data in a Digital
World (BD 2019), CEUR-WS. org, 2022.
[6] P. Ronzino, Harmonizing the crmba and crmarchaeo models, International Journal on
Digital Libraries 18 (2017) 253–261.
[7] M. Pasin, J. Bradley, Factoid-based prosopography and computer ontologies: towards an
integrated approach, Digital Scholarship in the Humanities 30 (2015) 86–97.
[8] E. Devecchi, Middle Babylonian Texts in the Cornell Collections, Part 2, Penn State Uni-
versity Press, 2020.
[9] T. Van den Hout, Administration and writing in hittite society, in: Balza, ME; Giorgieri,
M.; Mora, C.(a cura di), Archivi, depositi, magazzini presso gli Ittiti. Nuovi materiali e
nuove ricerche= Proceedings of the Workshop held at Pavia, 2009, pp. 41–58.
[10] D. Bamman, A. Anderson, N. A. Smith, Inferring social rank in an old assyrian trade
network, arXiv preprint arXiv:1303.2873 (2013).
[11] C. Waerzeggers, Social network analysis of cuneiform archives: A new approach, Docu-
mentary Sources in Ancient Near Eastern and Greco-Roman Economic History: Method-
ology and Practice (2014) 207–233.
[12] M. Newman, A.-L. Barabasi, D. J. Watts, The structure and dynamics of networks, vol-
ume 12, Princeton University Press, 2011.
[13] A. Li, S. P. Cornelius, Y.-Y. Liu, L. Wang, A.-L. Barabási, The fundamental advantages of
temporal networks, Science 358 (2017) 1042–1046.
[14] T. Zhou, J. Ren, M. Medo, Y.-C. Zhang, Bipartite network projection and personal recom-
mendation, Physical review E 76 (2007) 046115.
[15] M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, M. A. Porter, Multilayer
networks, Journal of complex networks 2 (2014) 203–271.
[16] A. Aleta, Y. Moreno, Multilayer networks in a nutshell, Annual Review of Condensed
Matter Physics (2018).
[17] S. Bornhofen, M. Düring, Exploring dynamic multilayer graphs for digital humanities,
Applied Network Science 5 (2020) 1–13.
[18] J. Novak, I. Micheel, M. Melenhorst, L. Wieneke, M. Düring, J. G. Morón, C. Pasini,
M. Tagliasacchi, P. Fraternali, Histograph – a visualization tool for collaborative anal-
ysis of networks from historical social multimedia collections, in: 2014 18th International
Conference on Information Visualisation, 2014, pp. 241–250. doi:1 0 . 1 1 0 9 / I V . 2 0 1 4 . 4 7 .
[19] K. Das, S. Samanta, M. Pal, Study on centrality measures in social networks: a survey,
Social network analysis and mining 8 (2018) 1–11.
[20] S. Fortunato, Community detection in graphs, Physics reports 486 (2010) 75–174.
[21] R. L. Breiger, P. E. Pattison, Cumulated social roles: The duality of persons and their
algebras, Social networks 8 (1986) 215–256.
[22] K. Mizoguchi, Nodes and edges: a network approach to hierarchisation and state forma-
tion in japan, Journal of Anthropological Archaeology 28 (2009) 14–26.
[23] M. Schich, C. Song, Y.-Y. Ahn, A. Mirsky, M. Martino, A.-L. Barabási, D. Helbing, A
network framework of cultural history, science 345 (2014) 558–562.
[24] P. Ribeiro, F. Silva, Discovering colored network motifs, in: Complex networks V,
Springer, 2014, pp. 107–118.