Analyzing future communities in growing citation networks

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Citation networks contain temporal information about what researchers are interested in at a certain time. A community in such a network is built around either a renowned researcher or a common research field; either way, analyzing how the community will change in the future will give insight into the research trend in the future. The paper views the research community as a Social Web where the communication is through academic papers. The paper proposes methods to analyze how communities change over time in the citation network graph without additional external information and based on node and link prediction and community detection. Different combinations of the proposed methods are also analyzed. The identified communities are classified using key term labeling. Experiments show that the proposed methods can identify the changes in citation communities multiple years in the future with performance differing according to the analyzed time span. Furthermore, the method is shown to produce higher performance when analyzing communities to be disbanded and to be formed in the future.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2014-10
Language
English
Article Type
Article
Keywords

LINK-PREDICTION; TOPICS; MODEL

Citation

KNOWLEDGE-BASED SYSTEMS, v.69, pp.34 - 44

ISSN
0950-7051
DOI
10.1016/j.knosys.2014.04.036
URI
http://hdl.handle.net/10203/193925
Appears in Collection
IE-Journal Papers(저널논문)
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