Using Names Lists for Social Network Analysis
10.6092/issn.2532-8816/9040
In questo paper viene presentato l'utilizzo di liste digitali di nomi provenienti da fonti analogiche, per l'analisi delle reti sociali. Attraverso alcuni esempi di liste, provenienti dalle collezioni d'archivio e strumenti di corredo, si intende mostrare come l'analisi delle reti sociali possa evidenziare relazioni e contraddizioni fra diversi dataset di nomi o possa essere utilizzata per evidenziare relazioni all'interno di un singolo dataset. Strumenti per la visualizzazione di dati come Gephy supportano questo tipo di analisi e presentano le relazioni individuate in maniera facilmente comprensibile. L'esposizione delle relazioni tra collezioni o all'interno di una singola collezione ne aumenta le possibilità di individuazione.
text/html
2532-8816
9040
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en
Megan Lewis
Umanistica Digitale
Using Names Lists for Social Network Analysis
Text.Serial.Journal
https://umanisticadigitale.unibo.it/
Articles
2019-03-01
2019-02-04
2019-03-01
2019-12-18
Um. Dig.
Megan Lewis
2019/03/01
10.6092/issn.2532-8816/9040
U.S. Holocaust Memorial Museum, Washington DC, US
2532-8816
4
en
Using Names Lists for Social Network Analysis
https://umanisticadigitale.unibo.it/article/view/9040/8939
    Umanistica Digitale - ISSN:2532-8816 - n.4, 2019
M. Lewis – Using Names Lists for Social Network Analysis
DOI: http://doi.org/10.6092/issn.2532-8816/9040


       Using Names Lists for Social Network Analysis

                                              Megan Lewis

                        U.S. Holocaust Memorial Museum, Washington DC, US
                                          mlewis@ushmm.org




       Abstract. In this paper, I discuss using digital names lists compiled from analog sources
       for social network analysis. Using examples from finding aids of archival collections, I
       demonstrate how social network analysis can show relationships and contrasts between
       different datasets of names or be used to show relationships within a single set of names.
       Data visualization tools such as Gephi aid in the analysis and present the relationships in
       an easily understandable way. Exposing relationships between collections or within a
       collection increases its findability.
       In questo paper viene presentato l'utilizzo di liste digitali di nomi provenienti da fonti
       analogiche, per l'analisi delle reti sociali. Attraverso alcuni esempi di liste, provenienti
       dalle collezioni d'archivio e strumenti di corredo, si intende mostrare come l'analisi delle
       reti sociali possa evidenziare relazioni e contraddizioni fra diversi dataset di nomi o possa
       essere utilizzata per evidenziare relazioni all'interno di un singolo dataset. Strumenti per la
       visualizzazione di dati come Gephy supportano questo tipo di analisi e presentano le
       relazioni individuate in maniera facilmente comprensibile. L'esposizione delle relazioni
       tra collezioni o all'interno di una singola collezione ne aumenta le possibilità di
       individuazione.




       Introduction

Holocaust records are full of lists - deportations, ghetto residents, camp prisoners. List-making
continued into the postwar period with lists of survivors published by various organizations,
memorial lists of those who died, displaced person records and emigration records. However,
these lists were made in analog format making analysis extremely difficult. In the last two
decades, these lists have been indexed and placed online.
Memorialization is a primary motivation. The Jewish genealogical community has been
particularly active in indexing. As prominent Jewish genealogist Arthur Kurzweil wrote, “When
the Nazis rounded up our relatives, they took away their names and gave them numbers. What
we genealogists are with is taking away the numbers and giving them back their names.”5..
Indexes can range from one list from one event in a certain town to complete websites


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compiled from multiple sources, such as the memorial to 600 men deported from Putten, the
Netherlands 8..
Names lists can be also gleaned from finding aids where an archivist made the effort to list
individual names. Oftentimes, names list based finding aids consist of individual case files,
such as in the United Restitution Organization files discussed later. However, some
organizations like the Zydowski Instytut Historyczny Im. Emanuela Ringelbluma in Warsaw
create appendices to finding aids that list the names of individuals mentioned in the collection 1.


        Using Finding Aids

While archival finding aids have been available online since the early days of the internet, the
electronic versions usually mimicked the format of their paper predecessors and only leveraged
technology for searching and browsing of subject headings and keywords. The advent of
Encoded Archival Description (EAD) data standard for the description of archival and other
unpublished material allows researchers to find related collections within and across
repositories. Some archives have experimented with using social navigation tools such as
recommending and collaborative filtering 9.. Several papers and projects explored applying data
visualization techniques to help archival users find related collections within a repository. A
recent project by Anne Bahde applied data visualization such as GoogleFusion and
TimeMapper to finding aids in an effort to help researchers better understand a collection’s
scope and to refer them to related collections 2.. The Social Networks and Archival Context
(SNAC) project in particular focuses on finding individuals within diverse archival collections
and connecting them within networks. As of July 2017, SNAC does not contain any data
visualization tools 1..


        Social Network Analysis

Social network analysis is looking at sets of people, groups, places and things and mapping the
interactions or relationships between them. In Holocaust studies, some examples would be the
relation between a camp and its sub-camps, residents and housing within a ghetto or restitution
organizations and claimants. Using already extant names lists allows for analysis to happen
without the need to create new data sets. Data visualization provides an efficient graphical way
to present the analysis.
This project used Gephi, an open source analysis and visualization tool. Gephi was chosen
because it is free, open-source, runs on both Mac and Windows platforms and, most
importantly, has a robust user community that provides extensive tutorials and other support.

    1    For an example, see the index of names for the American Joint Distribution Committee in
         Poland 1945-1949 records created by ZIH archivists Tadeusz Epsztein and Agnieszka Reszke at
         https://collections.ushmm.org/findingaids/RG-15.120M_03_idx_pl.pdf. A copy of this
         collection is held at the United States Holocaust Memorial Museum Archives as RG-15.120.


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The author started the project with minimal exposure to data visualization tools but was able to
teach herself Gephi using the tools available 3..


        Analysing Names Lists


        Separate but similar collections

Holocaust archives often contain collections that are related by topic or creator. Camp ledgers
of incoming prisoners would be one example. Records of restitution organizations are another.
The United Restitution Organization, founded in 1948, provided legal assistance to Holocaust
survivors with their restitution and reparation claims against the West German government 7..
At its largest, the URO had offices in West Germany, Israel, the United States, Canada,
Argentina, Sweden, Great Britain, Belgium, France and several other countries. In countries
with small survivor populations, the URO partnered with other Jewish organizations to provide
legal services to survivors 4.. The United States Holocaust Memorial Museum Archives holds
the original paper files of the United Restitution Organization (URO) files for their Los
Angeles, Buenos Aires and Toronto offices. The finding aids for Los Angeles and Toronto are
Excel spreadsheets that include first and last names of claimant, their date of birth, and place of
birth. The Toronto finding aid also includes information on which specific program applied to
a claimant2. Using a sample of 738 claimants from Los Angeles and 535 claimants from
Toronto, the author did an analysis to determine if there was any link between a claimant’s
home country and which URO office he or she used to file the claim. The analysis was
directed, meaning that the links between nodes were in one direction, in this case countries of
origin to URO office. In an undirected analysis the links between nodes can go either way, such
as when analyzing correspondence between different people.
From the resulting graph visualization, the following observations can be quickly made:
         1.   A greater percentage of Austrians and Germans used the Los Angeles office,
              which is not surprising since there was a large German-speaking émigré
              community in the Los Angeles area.
         2.   The Los Angeles data is less complete, as the number of claimants with an
              unknown place of birth is much larger than for Toronto.
         3.   Survivors from Poland included in these example datasets were more likely to
              apply using the Toronto office, again not surprising because of immigration
              patterns.
The limited data included in the Los Angeles URO finding aid precludes any further in-depth

    2    The Toronto URO files are USHMM RG-28.005,
         https://collections.ushmm.org/search/catalog/irn503540. The Los Angeles URO files are
         USHMM RG-28.004, https://collections.ushmm.org/search/catalog/irn503539. The Buenos
         Aires URO files finding aid lists only claimant’s name.


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comparison with the Toronto data. However, further work could be done using the Toronto
data alone, for example by determining what percentage of claimants applied to each claim
program or to determine how many claimants applied to multiple programs.


         Visualizing data from one collection
One of the largest collections in the United States Holocaust Memorial Museum Archives is
RG-14.070, the General State Prosecutors Office of the State Court of Berlin records (Berlin
Landesarchiv Fond A Rep. 358.)3




Figure 1: Visualization showing home countries of claimants to the Los Angeles and Toronto URO offices


This collection contains approximately 150,000 cases from the Berlin prosecutor’s office from
1933 to 1945. The cases include both Nazi-specific crimes (political offenses, race defilement)
and ordinary crime (theft, manslaughter, illegal duplication of keys.) The finding aid is an
Access database that allows for each searching but not for easy analysis of the cases. The Berlin
Landesarchiv developed codes


     3    Additional information on this collection is at
          https://collections.ushmm.org/search/catalog/irn517822


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                                         M. Lewis – Using Names Lists for Social Network Analysis


Victim type                                           Special Punishment
Ausländer (AL) Foreigner                              Asocial Sicherungsverwahrung(SV)
                                                      Preventative detention
Juden (JD) Jew                                        Heil- u. Pflegeanstalten (HP) Mental
                                                      institution
Homosexuelle(HS)                                      Euthanasie (EU) Euthanized
Homosexual
Politisch Verfolgte (PO)                              Kastration (KA) Castrated
Political persecutee
Gewohnheitsverbrecher (GV) habitual                   Todesurteil (TD) Death Sentence
criminal
Deserteure (DS) Deserters                             Hinrichtung (HR) Execution
Sinti u. Roma (SR) Roma and Sinti                     Straflagerhaft (SL) Prison Camp
Bibelforscher (BS) Jehovah's Witness                  Einweisung in ein KZ (XKZ) Concentration
                                                      camp
Asozialer (AS)
Table 1: List of special codes in finding aid for RG-14.070




        Network based on punishment
Castration was used as a special punishment and 130 were subjected to it. Looking at these
cases, the following questions can be asked: What crimes did they commit? Did these men
commit multiple crimes? What other punishments did they receive?
Using the same set of data, the following three graph visualizations were created. The first
graph charted what types of crimes the castrated men committed. Using statistical tools built
into the Gephi program, number of instances of each type of crime were statistically weighted.
The weight of each crime is indicated by both node color and size. The author was surprised to
learn from this graph that the third most common crime committed that lead to castration was
exhibitionism/creating a public disturbance. The most common two crimes were not
surprisingly child sex abuse and homosexuality.
The following graphs demonstrate the importance of direction. In creating a graph in Gephi,
one set of variables is the source and the other set is the target. Switching the source and the
target within a directed graph provides very different results. 3 visualizes the data when the type
of crime is the source and the individuals are the targets. In this example individuals who
committed more than one crime are not connected to both crimes. While this graph gives a
good snapshot of the different crimes available it does not display the network very well.


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In 3, the directed relationship was reversed. The individuals were the source and the types of
crime were the target. This graph allows one individual to be connected to more than one type
of crime and better illustrates the relationship between individuals and crimes. From this graph,
a researcher can tell that a sizable percentage of the castrated men were charged with both
homosexuality and child sex abuse. The use of the Fruchterman-Rheingold algorithm on the
data in this graph forces the more connected crimes and the crimes with more instances to the
center of the graph.
3 also displays another feature of social network analysis graphs- the ability to assign different
attributes to nodes and indicate them by using different colors. In this figure, the different
attributes indicate any additional punishments applied to the individual. In the case of the
castrated men, the most common additional punishments were being taken into protective
custody and being sent to a concentration. By making the effort to include attributes in a data
visualization graph based on a finding aid, an archivist can expose the collection to researchers
who may not otherwise consider the collection as a resource. For example, the castrated men
may be of interest to historians studying Dr. Carl Peter Jensen, aka Carl Vaernet, a Danish
doctor who conducted pseudo-medical experiments on artificial hormone implants in the
Mauthausen concentration camp4. He used castrated men in some of his experiments. A brief
search for five of the men sent to concentrations camps in the International Tracing Service
records revealed that two died in Mauthausen. Additional research would be needed in the
Mauthausen records to determine if these men were part of Carl Vaernet experiments.
Researchers studying mental institutions in the Nazi era, which were part of the euthanasia
program, now have several additional names to research.
The networks around the edge of the graph in Figure 5 show the less common types of crimes
and other investigations, plus additional punishments. Richard H. was arrested for theft and
later was euthanized. Willi B. resisted law enforcement but suffered no additional special
punishment. The Prosecutors Office investigated one suicide and one case of legal
incapacitation within this group of men.
The General State Prosecutors records are an ideal candidate for additional analysis and
visualization because of the inclusion of the special codes by the creator of the finding aid. This
author is currently working on visualizations using cases that involved Rassenschande (race
defilement) and individuals given the designation GV, Gewohnsheitverbrecher, or habitual
criminal. Initial results of the GV analysis review that only a small percentage of the individuals
were charged under paragraph 20a of the German criminal code which defined the conditions
for the designation to be applied.




4   See 6..



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Figure 2: Graph of crimes statistically weighted both in nodes (with different colors) and edges (in brown)




Figure 3: Graph of the instances of each type of crime with the crime type being the source and the
individual being the target




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Figure 4: Graph of crimes and individuals with the added attribute indicating additional punishments.
The direction of the graph is from person to the crime.


Unfortunately, most finding aids are not as rich in detail. This author envisions more complex
network analyses that can be made, such as comparing a memorial names list created from
testimonial sources to a list from a finding aid of a collection from the same place, analyzing
lists from two archival finding aids about related topics from different organizations, and an
analysis of two lists based on testimonial of victims from the same place. An extremely complex
analysis would involve survivors originally from Austria who used the services of the United
Restitution Office, the list of Austrian Jews who filed property declarations known as Recht als
Unrecht (which has both memorial and finding aid functions), the list of Jews whose
automobiles were confiscated created by the Vienna Technical Museum and the lists of
claimants on the Austrian government website findbuch.at.


        Challenges

Using names lists on memorial sites and in finding aids holds promise of interesting findings.
However, researchers wanting to use these sources face several challenges.



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       Lack of Available Linked Open Data
A major challenge is the lack of linked open data freely available on the internet. Most names
data available is only accessible to the public via a search interface, making the pulling full lists
difficult. Some finding aids are only available at the host institution. Such is the case with the
United Restitution Organization records at the United States Holocaust Memorial Museum.
This restricted access exists for several possible reasons. One reason is the issue of privacy and
data protection laws. Another reason is the desire of the owning institution to know who is
using their data. Finding aids can contain personally identifiable information such as dates of
birth. The owners of the three collections used in the data visualizations in this paper all require
researchers to sign user declaration stating that the researcher understands and will comply with
the rules and regulations of the institution with regards to privacy, providing copies of
published works, and copyright. For the hypothetical complex Austrian example mentioned
above, only Recht als Unrecht is available online as a complete dataset, and even then, it is
buried on the website of the Jewish genealogy publisher Avotaynu. The URO finding aids are
only available onsite at the United States Holocaust Memorial Museum, the Vienna Technical
Museum database is only available via a search interface, and Findbuch.at requires a registration
process that including submitting a scan of a passport.


       Data normalization
Another challenge is data normalization between or within a dataset. Often the challenge is a
matter of time, as normalization can be time consuming. While some normalization can be
automated, such as dates, other data types require human intervention. Place names commonly
need to be normalized requiring human intervention. One of the claimants in the URO
Toronto records listed her place of birth as Czernowitz, Ukraine, combining the pre-1918 place
name with the post-1992 country, even though at the time of her birth the city was officially
Cernauti, Romania. Part of the normalization process is determining what standard to use.


       Conclusion

Names lists that are part of memorials or are found in archival finding aids provide datasets for
social network analysis. When turned into data visualizations, researchers can discover
previously unseen connections. In addition, visualizations can increase a list’s or archival
collection’s findability and expand their use in research.


       Acknowledgments

My thanks to the organizers of the conference “Data Sharing, Holocaust Documentation and
Digital Humanities” for this opportunity and for their comments. This project started as part
of my course work for a post-MLS certificate in the Curation and Management of Digital
Assets at the University of Maryland iSchool, College Park, MD.


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         References

    1.    “About SNAC,” Social Network and Archival Context,
          http://socialarchive.iath.virginia.edu/about.html
    2.    Bahde, Anne. 2017. “Conceptual Data Visualization in Archival Finding Aids:
          Preliminary User Responses.” Libraries and the Academy 17:3, pp. 485-506.
          https://preprint.press.jhu.edu/portal/sites/ajm/files/17.3bahde.pdf
    3.    “Gephi: makes graphs handy,” https://gephi.org/.
    4.    History of the United Restitution Organization,
          https://www.cjarchives.ca/archives/uro/history/history.htm .
    5.    Kurzweil, Arthur. 2004. “Introduction.” In Avotaynu Encyclopedia of Jewish Genealogy:
          Guide to Jewish Genealogy, ed. by Sallyann, Amdur Sack and Gary Mokotoff.
          Bergenfield, NJ: Avotaynu.
    6.    Pasternak, Alfred. 2006. Inhuman Research. Medical experiments in German
          concentration camps. Budapest: Akademiai Kiadó, p. 360.
    7.    Rothfeld, Anne. 2000. “A Source for Holocaust Research: The United Restitution
          Organization Files.” Perspectives on History, https://www.historians.org/publications-
          and-directories/perspectives-on-history/april-2000/a-source-for-holocaust-research-
          the-united-restitution-organization-files.
    8.    Stichting Oktober 44, http://www.oktober44.nl/monument
    9.    Yakel, Elizabeth, Seth Shaw and Polly Reynolds. 2007. “Creating the Next Generation
          of Archival Finding Aids,” D-Lib Magazine 13:5/6,
          http://www.dlib.org/dlib/may07/yakel/05yakel.html.


Last URLs access: July 20, 2017




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