SOCIAL NETWORK ANALYSIS FOR DIGITAL HUMANITIES

Current trends in academia show that a key factor for tackling complex problems and doing
successful research is interdisciplinarity. With the increasing availability of digital tools
and online databases, many disciplines in the humanities and social sciences are seeking
to incorporate computational techniques in their research workflow. Digital humanities
(DH) is a collaborative and interdisciplinary area of research that bridges computing and
the humanities disciplines, bringing digital tools to humanities scholars to use, together
with a critical understanding of such tools. Social network analysis is one of such tools.
Social network analysis focuses on relationships among social actors and it is an important
addition to standard social and behavioral research, which is primarily concerned with
attributes of the social units.
In this work we present the field of digital humanities and its current challenges, as well
as an overview of the most recent trends in historical network research, emphasizing the
advantages of using social network analysis in history and the missed opportunities. We
then present the field of network analysis, providing a formalization of the concept of social
network, models that explain the mechanism governing complex networks and tools such
as network metrics, orbit analysis and Exponential Random Graph Model.
We tackle the problem of community detection. We propose MemLPA, a new version of
the label propagation algorithm, by incorporating a memory element, in order for nodes
to consider past states of the network in their decision rule. We present a use case, drawn
from the collaboration with a historian colleague, showing how social network analysis can
be used to answer research questions in history. In particular, we addressed the gender and
ethnic bias problem in computer science research by looking at different collaboration patterns in the temporal co-authorship network. Finally, we present another use case, based on
collaboration data collected at the National Electronics and Computer Technology Center
(NECTEC) in Thailand. We build a temporal collaboration network where researchers are
connected if they worked together on one or more artifacts, focusing on measuring productivity and quality of research and development, while linking these metrics to the structure
of the collaboration network.