• Corpus ID: 18805281

Bio-inspired Methods for Dynamic Network Analysis in Science Mapping

@article{Sos2011BioinspiredMF,
  title={Bio-inspired Methods for Dynamic Network Analysis in Science Mapping},
  author={S{\'a}ndor So{\'o}s and George Kampis},
  journal={ArXiv},
  year={2011},
  volume={abs/1101.3684}
}
We apply bio-inspired methods for the analysis of different dynamic bibliometric networks (linking papers by citation, authors, and keywords, respectively). Biological species are clusters of individuals defined by widely different criteria and in the biological perspective it is natural to (1) use different categorizations on the same entities (2) to compare the different categorizations and to analyze the dissimilarities, especially as they change over time. We employ the same methodology to… 

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References

SHOWING 1-10 OF 13 REFERENCES

Mapping of science by combined co-citation and word analysis. II: Dynamical aspects

TLDR
It is shown that, over a period of 10 years, continuity in intellectual base was at a lower level than continuity in topics of current research, which indicates that a series of interesting new contributions are made in course of time, without vast alteration in general topics of research.

Mapping of science by combined co-citation and word analysis, I. Structural aspects

The claim that co-citation analysis is a useful tool to map subject-matter specialties of scientific research in a given period, is examined. A method has been developed using quantitative analysis

Combining Mapping and Citation Analysis for Evaluative Bibliometric Purposes: A Bibliometric Study

The general aim of the article is to demonstrate how the results both of a structural analysis, and of a research performance assessment of a research field, can be enriched by combining elements of

Community structure in social and biological networks

  • M. GirvanM. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2002
TLDR
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.

Finding and evaluating community structure in networks.

  • M. NewmanM. Girvan
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
TLDR
It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.

A review of bibliometric and other science indicators and their role in research evaluation

  • J. King
  • Environmental Science
    J. Inf. Sci.
  • 1987
TLDR
In this state-of-the- art review the various methodologies that have been developed are outlined in terms of their strengths, weaknesses and par ticular applications.

Modularity and community structure in networks.

  • M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2006
TLDR
It is shown that the modularity of a network can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which is called modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times.

19 A hierarchy of species concepts : the denouement in the saga of the species problem

  • R.
  • Environmental Science
  • 2009
At least 22 concepts of species are in use today and many of these are notably incompatible in their accounts of biological diversity. Much of the traditional turmoil embodied in the species problem

Computing Communities in Large Networks Using Random Walks

TLDR
A measure of similarities between vertices based on random walks which captures well the community structure in a network, can be computed efficiently, and it can be used in an agglomerative algorithm to compute efficiently thecommunity structure of a network.

Objective Criteria for the Evaluation of Clustering Methods

TLDR
This article proposes several criteria which isolate specific aspects of the performance of a method, such as its retrieval of inherent structure, its sensitivity to resampling and the stability of its results in the light of new data.