Application of Graph Entropy for Knowledge Discovery and Data Mining in Bibliometric Data

Entropy, originating from statistical physics, is an interesting and challenging concept with many diverse definitions and various applications. This chapter explores the problems of bibliometrics and how entropy could be used to tackle these problems. It investigates 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, the chapter shows how different graphs can be constructed from bibliometric data and what research problems can be addressed by each of those. It focuses on coauthorship graphs to identify collaboration styles using graph entropy. For this purpose, a subgroup of the DBLP database is selected and prepared for analysis. The results show how two entropy measures describe data set. From these results, the chapter concludes the discussion of the results and considers different extensions on how to improve approach.