Page 185 - JOURNAL OF LIBRARY SCIENCE IN CHINA 2018 Vol. 44

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184 Journal of Library Science in China, Vol.10, 2018

Information Science. Exploring the emergence of correlations in the process of knowledge growth
with network science thinking can reveal the growth patterns and mechanisms of knowledge
correlations. The present research extracts a total of over 440,000 pairs of knowledge correlations,
870,000 times of correlation frequency, which are divided into 11 time windows. The small-world
and scale-free properties of time series domain knowledge network topology are determined by the
short average path length, the high clustering coefficient and the power-law distribution of degree.
On this basis, the knowledge correlations and frequencies in the process of domain knowledge
growth are tracked and analyzed from the aspects of the number of correlation frequencies, the
proportion of correlation frequencies, the status of neighboring windows, etc.
Results have shown that in the process of knowledge correlation growth, the frequency
distribution of correlations is in accordance with power-law. Through the time series analysis of
the frequency of knowledge correlations, it is found that the frequency distribution of knowledge
correlations in the same network can better fit the power-law than the degree distribution of nodes,
without the phenomenon of “top-heavy distribution” usually found in the degree distribution
of nodes, and the power-law distribution of the frequency of knowledge correlations performs
better in the latter part of the time series. This phenomenon shows that although the knowledge
correlations determine the topology of knowledge networks, only a few correlations have
extremely high frequency values, while most of them have only a small number of frequency
values, and the process of frequency of knowledge correlations is that of frequency emergence.
On the other hand, the growth process of knowledge correlations has the property of “the rich gets
richer” in frequency, and mainly follows the mechanism of “preferential reinforcement”.
Statistics show that as the frequency of knowledge correlations grows and accumulates, the few
frequency “rich” (high-frequency correlation) occupy more and more frequency “wealth” than
most frequency “poor” (low-frequency correlation). The gap between the “rich” and the “poor”
in frequency of knowledge correlations becomes more and more obvious in the second half of the
timeline as the domain knowledge grows and develops. The status of the neighboring windows
shows that most of the knowledge correlations in the frequency “extremely rich” status often
have the “extremely rich” status in the previous time window. Correlations with more frequency
“wealth” will attract additional frequencies with a higher probability in the process of growth and
development of domain knowledge, reflecting the frequency growth mechanism of “preferential
reinforcement”. The repeated superimposition of the micro-rules of “preferential reinforcement”
on the time series have created the phenomena of “the rich get richer” at the frequency level in
the emergence of correlation. The study has also found the “bursts reinforcement” phenomenon
that the correlations in frequency “extremely poor” status suddenly jump into the “extremely rich”
status. The main reason for this phenomenon is that academia has produced significant findings or
inventions, which provide a possibility for identifying significant academic achievements based on
the frequency of knowledge correlation.
Although the knowledge networks based on social tagging system used in this study are not