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Article overview
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Ground truth? Concept-based communities versus the external classification of physics manuscripts | | Vasyl Palchykov
; Valerio Gemmetto
; Alexey Boyarsky
; Diego Garlaschelli
; | | Date: |
6 Feb 2016 | | Abstract: | Community detection techniques are widely used to infer hidden structures
within interconnected systems. Despite demonstrating high accuracy on
benchmarks, they reproduce the external classification for many real-world
systems with a significant level of discrepancy. A widely accepted reason
behind such outcome is the unavoidable loss of non-topological information
(such as node attributes) encountered when the original complex system is
represented as a network. In this article we emphasize that the observed
discrepancies may also be caused by a different reason: the external
classification itself. For this end we use scientific publication data which i)
exhibit a well defined modular structure and ii) hold an expert-made
classification of research articles. Having represented the articles and the
extracted scientific concepts both as a bipartite network and as its unipartite
projection, we applied modularity optimization to uncover the inner thematic
structure. The resulting clusters are shown to partly reflect the author-made
classification, although some significant discrepancies are observed. A
detailed analysis of these discrepancies shows that they carry essential
information about the system, mainly related to the use of similar techniques
and methods across different (sub)disciplines, that is otherwise omitted when
only the external classification is considered. | | Source: | arXiv, 1602.8451 | | Services: | Forum | Review | PDF | Favorites | |
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