By uncovering the hierarchical structure of scholarly citation, we can identify key papers
pertaining to any search query.
For a reader new to the field we can find the classic and foundational papers; for an expert we can find the latest
By integrating a hierarchical clustering of citation networks with semantic analysis, we develop a scalable map
of scientific fields and the key research terms and topics therein.
Scientific influence is often quantified using simple citation counts, but the structure of a citation network provides
far more information than can be revealed by these simple counts. This is principle behind the Eigenfactor metrics
we can better rank the importance of scientific journals
or papers by viewing them in the context of the full citation
Classifying knowledge domains is often carried out by appeal to established disciplinary boundaries.
But as defined by the purview of academic departments or the scope of scholarly journals, the fields and
subfields of scientific research reflect not so much the current structure of science
but rather its past history.
Networks of recent citations provide a more up-to-date view of the topography of science.
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