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Life-Cycles and Mutual Effects of Scientific Communities
Vaclav Belák, Marcel Karnstedt, Conor Hayes
Digital Enterprise Research Institute, NUI Galway
{vaclav.belak, marcel.karnstedt, conor.hayes}@deri.org
1. Introduction
Claims for progress in a scientific community are
generally assessed using cumulative citation measures.
However, the analysis of the life-cycle of a community
provides much greater explanatory power for the
progress and potential of a scientific field. While
previous work has examined scientific networks
through co-citation and textual analysis, there is
relatively little work on analysing the dynamics of
cross-community behaviours, particularly where closely
related communities are competing for scientific,
funding and industrial capital.
Figure 1 Community shift (left) and merge (right)
Inspired by Thomas Kuhn's work [1], we identified
several interesting cross-community phenomena, which
we then mined in an automated manner. For example, a
new community with a distinct topic can emerge from
an established research community, where the emerging
topic can be based on a novel approach or method. We
call this phenomenon a community shift (see Fig 1).
Similarly, a community can merge with another one. A
community can also move in time from broader topics
to more specific ones, which we call community
specialization.
2. Methodology
We extracted co-citation network of 5772 scientists
from papers published between 2000-2009 in two
related disciplines in computer science: Semantic Web
(SW) and Information Retrieval (IR). We then divided
the network into ten overlapping time-slices and
identified communities in each slice using Infomap [2]
and Louvain [3] methods. The communities were
matched across the slices according to the highest
Jaccard coefficient, and important ancestors and
descendants were identified for each community using
measures derived from Jaccard coefficient.
Additionally, keywords were extracted from the
papers, for which the text version was available,
resulting in nearly 70% coverage of the network by the
content.
Finally, we applied several specifically tailored
measures combining both structural and content
features in order to detect the interesting phenomena.
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3. Results
An emergence of a trans-disciplinary community
(community 15) that bridged the Semantic Web and
Information Retrieval fields was detected between
2004-2007. This community was formed mainly by
former members of Semantic Web community 0
depicted with red colour in the left part of the snapshots
in Fig 2. We identified that the main research topic of
community 15 had been Semantic Web until 2006–
2008, during which time information retrieval became
one of its core topics. At the same time this topic
disappeared for its ancestor community. In 2007 the
whole community moved between the SW and IR
communities (see Fig 2), which is supported by
investigated rise of its normalized group betweenness
from 0.09 in 2004 to 0.27 in 2007. Therefore, whereas
community 0 kept its focus on the core SW-related
topics, it also formed a new interdisciplinary
community, which has functioned since then as a
mutual intermediary between SW and IR communities.
Analysis of different overlap measures revealed that an
effort to establish this interdisciplinary collaboration
came mainly from the SW community. Our approach
uses community-finding techniques in combination
with different overlap measures, special visualisations
and automated metadata extraction and has enabled us
to identify several other similar cases to support the
hypotheses introduced above [4].
Figure 2 Network snapshots in 2004 (left) and 2007 (right). Note the
central position of the transdisciplinary community 15 (violet).
4. References
[1] Kuhn, T., Structure of Scientific Revolutions, University of
Chicago Press, Chicago, USA, 1962
[2] Rosvall M., Bergstrom C., Maps of information flow
reveal community structure in complex networks, PNAS, 2008
[3] Blondel, V. et al., Fast unfolding of communities in large
networks, J. of Stat. Mech.: Theory and Experiment, 2008
[4] Belák,V et al., Cross-Community Dynamics in Science,
arXiv:1010.4327, 2010
Acknowledgements
The material presented in this work is based upon works
jointly supported by the Science Foundation Ireland under
Grant No. SFI/08/CE/I1380 (Lion-2) and under Grant No.
08/SRC/I1407 (Clique: Graph & Network Analysis Cluster).