Abstract
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 |
|---|---|
| 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 |
|---|---|
| Publisher | Wiley-VCH |
Fingerprint
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
Cite this
Application of Graph Entropy for Knowledge Discovery and Data Mining. / Calero Valdez, André; Dehmer, Matthias; Holzinger, Andreas.
Mathematical Foundations and Applications of Graph Entropy. ed. / Matthias Dehmer; Frank Emmert-Streib; Zengqiang Chen; Xueliang Li; Yongtang Shi. John Wiley & Sons, Inc, 2016. p. 259-276 (Quantitative and Network Biology Series).Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review
}
TY - CHAP
T1 - Application of Graph Entropy for Knowledge Discovery and Data Mining
AU - Calero Valdez, André
AU - Dehmer, Matthias
AU - Holzinger, Andreas
PY - 2016/9/24
Y1 - 2016/9/24
N2 - 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 processesNext, 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 measuresdescribe our data set. From these results, we conclude our discussion of theresults and consider different extensions on how to improve our approach.
AB - 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 processesNext, 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 measuresdescribe our data set. From these results, we conclude our discussion of theresults and consider different extensions on how to improve our approach.
KW - Knowledge Discovery
KW - Machine Learning
KW - entropy
KW - Graph entropy
UR - http://eu.wiley.com/WileyCDA/WileyTitle/productCd-3527339094.html
M3 - Chapter
SN - 978-3-527-33909-9
T3 - Quantitative and Network Biology Series
SP - 259
EP - 276
BT - Mathematical Foundations and Applications of Graph Entropy
A2 - Dehmer, Matthias
A2 - Emmert-Streib, Frank
A2 - Chen, Zengqiang
A2 - Li, Xueliang
A2 - Shi, Yongtang
PB - John Wiley & Sons, Inc
ER -