Page 171 - JOURNAL OF LIBRARY SCIENCE IN CHINA 2018 Vol. 43

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Extended English abstracts of articles published in the Chinese edition of Journal of Library Science in China 2017 Vol.43 171

Correlation-driven domain knowledge community growth

〇a*
TENG Guangqing〇
The evolution of domain knowledge has always been the focus of the library and information
academia. Exploring the evolution of structural relationships in the process of knowledge
development with network thinking has become the consensus of academia. The evolution
process of the domain knowledge communities can be tracked and analyzed in view of the
changes of the relationships among knowledge units to reveal the growth pattern and the law of
the domain knowledge communities. This study makes an extraction of 2,075 related articles
in specific knowledge domains in folksonomy knowledge organization mode with 203 tags and
the correlations number 2,953 in total. The time span is from 2005 to 2015 and the timer shaft is
divided into 11 units of time-window. The original domain knowledge networks are constructed
based on the correlations between tags. Using the frequency of correlation as the threshold, the
knowledge networks at level are extracted based on scale-free and fractal theory. The k-plexes
and cliques in the knowledge networks at level are calculated separately, and the loose knowledge
communities and close-knit knowledge communities are identified by k-plexes and cliques.
On this basis, the time series analysis on tags, correlations, k-plexes, cliques in the knowledge
networks is executed from the quantitative dimension. The long-term trends in the evolution of
domain knowledge communities are identified. The number of tags, the number of correlations,
the number of k-plexes, the average scale of k-plexes, the number of cliques, and the average
scale of the cliques in the original knowledge networks and the knowledge networks at level in
the key time windows are cross-discovered. The influence of knowledge correlation on the growth
of domain knowledge community is thus revealed. The results show that the basic growth path
of the domain knowledge community is “correlation→loose knowledge community→close-
knit knowledge community”. Knowledge correlation achieves the connection between the
knowledge nodes. When the knowledge correlations are rich to a certain extent, they structure the
loose knowledge communities identified by k-plexes. With the further enrichment of knowledge
correlations, the close-knit knowledge communities identified by cliques emerge. The increase
of the correlations between knowledge units is the guarantee of the quantity multiplication and
scale expansion in the growth of domain knowledge communities. Both quantitative evolution
analysis and cross-discovery show that in the case of the same scale of the knowledge network,
as long as the numbers of correlations continue to increase, the correlations will still lead to more
loose knowledge communities and close-knit knowledge communities. At the same time, the new
correlations have the opportunities to bridge the old k-plexes or cliques into larger k-plex or clique,
resulting in a decrease in the number of knowledge communities and an increase of scale. The
frequency of correlation and the accumulation of numbers are the filter of core cohesion during

* Correspondence should be addressed to TENG Guangqing, Email: tengguangqing@163.com, ORCID: 0000-0002-1053-0959