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## Abstract

Entropy, originating from statistical physics, is an interesting and challenging concept with many diverse definitions and various applications. Considering all the diverse meanings, entropy can be used as a measure of disorder in the range between total order (structured) and total disorder (unstructured) as long as by “order” we understand that objects are segregated by their properties or parameter values. States of lower entropy occur when objects become organized, and ideally when everything is in complete order, the entropy value is 0. These observations generated a colloquial meaning of entropy. In this chapter we investigate the state of the art in graph-theoretical approaches and how they are connected to text mining. This prepares us to understand how graph entropy could be used in data-mining processes

Next, we show how different graphs can be constructed from bibliometric data and what research problems can be addressed by each of those. We then focus on coauthorship graphs to identify collaboration styles using graph entropy. For this purpose, we selected a subgroup of the DBLP database and prepared it for our analysis. The results show how two entropy measures

describe our data set. From these results, we conclude our discussion of the

results and consider different extensions on how to improve our approach.

Next, we show how different graphs can be constructed from bibliometric data and what research problems can be addressed by each of those. We then focus on coauthorship graphs to identify collaboration styles using graph entropy. For this purpose, we selected a subgroup of the DBLP database and prepared it for our analysis. The results show how two entropy measures

describe our data set. From these results, we conclude our discussion of the

results and consider different extensions on how to improve our approach.

Original language | English |
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Title of host publication | Mathematical Foundations and Applications of Graph Entropy |

Editors | Matthias Dehmer, Frank Emmert-Streib, Zengqiang Chen, Xueliang Li, Yongtang Shi |

Publisher | John Wiley & Sons, Inc |

Pages | 259-276 |

ISBN (Electronic) | 978-3-527-69322-1 |

ISBN (Print) | 978-3-527-33909-9 |

Publication status | Published - 24 Sep 2016 |

### Publication series

Name | Quantitative and Network Biology Series |
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Publisher | Wiley-VCH |

## Keywords

- Knowledge Discovery
- Machine Learning
- entropy
- Graph entropy

## ASJC Scopus subject areas

- Computer Science Applications

## Fields of Expertise

- Information, Communication & Computing

## Treatment code (Nähere Zuordnung)

- Basic - Fundamental (Grundlagenforschung)
- Experimental

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