Structural discrimination of networks by using distance, degree and eigenvalue-based measures.
In chemistry and computational biology, structural graph descriptors have been proven essential for characterizing the structure of chemical and biological networks. It has also been demonstrated that they are useful to derive empirical models for structure-oriented drug design. However, from a more...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3391207?pdf=render |
| _version_ | 1818524450368258048 |
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| author | Matthias Dehmer Martin Grabner Boris Furtula |
| author_facet | Matthias Dehmer Martin Grabner Boris Furtula |
| author_sort | Matthias Dehmer |
| collection | DOAJ |
| description | In chemistry and computational biology, structural graph descriptors have been proven essential for characterizing the structure of chemical and biological networks. It has also been demonstrated that they are useful to derive empirical models for structure-oriented drug design. However, from a more general (complex network-oriented) point of view, investigating mathematical properties of structural descriptors, such as their uniqueness and structural interpretation, is also important for an in-depth understanding of the underlying methods. In this paper, we emphasize the evaluation of the uniqueness of distance, degree and eigenvalue-based measures. Among these are measures that have been recently investigated extensively. We report numerical results using chemical and exhaustively generated graphs and also investigate correlations between the measures. |
| first_indexed | 2024-12-11T05:57:14Z |
| format | Article |
| id | doaj.art-534b6eb9cb3b45cbaa555b35a30526d7 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T05:57:14Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-534b6eb9cb3b45cbaa555b35a30526d72022-12-22T01:18:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0177e3856410.1371/journal.pone.0038564Structural discrimination of networks by using distance, degree and eigenvalue-based measures.Matthias DehmerMartin GrabnerBoris FurtulaIn chemistry and computational biology, structural graph descriptors have been proven essential for characterizing the structure of chemical and biological networks. It has also been demonstrated that they are useful to derive empirical models for structure-oriented drug design. However, from a more general (complex network-oriented) point of view, investigating mathematical properties of structural descriptors, such as their uniqueness and structural interpretation, is also important for an in-depth understanding of the underlying methods. In this paper, we emphasize the evaluation of the uniqueness of distance, degree and eigenvalue-based measures. Among these are measures that have been recently investigated extensively. We report numerical results using chemical and exhaustively generated graphs and also investigate correlations between the measures.http://europepmc.org/articles/PMC3391207?pdf=render |
| spellingShingle | Matthias Dehmer Martin Grabner Boris Furtula Structural discrimination of networks by using distance, degree and eigenvalue-based measures. PLoS ONE |
| title | Structural discrimination of networks by using distance, degree and eigenvalue-based measures. |
| title_full | Structural discrimination of networks by using distance, degree and eigenvalue-based measures. |
| title_fullStr | Structural discrimination of networks by using distance, degree and eigenvalue-based measures. |
| title_full_unstemmed | Structural discrimination of networks by using distance, degree and eigenvalue-based measures. |
| title_short | Structural discrimination of networks by using distance, degree and eigenvalue-based measures. |
| title_sort | structural discrimination of networks by using distance degree and eigenvalue based measures |
| url | http://europepmc.org/articles/PMC3391207?pdf=render |
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