A Case Study on Citation Network Analysis

A Case Study on Citation Network Analysis

Copyright: © 2018 |Pages: 18
DOI: 10.4018/978-1-5225-3799-1.ch007
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Abstract

In this chapter, the authors present a case study of Network Analysis in the field of bibliometrics, focused on the identification of central academic articles based on complex network metrics that can be implemented with algorithms covered throughout this book. The authors analyze a scientific citation network and systematically obtain the most central papers considering different perspectives of the selected document collection. Later, they discuss the potential benefits that the parallel kernels and the topology-aware partitioning algorithms can offer in the context of the presented study case. Finally, the authors summarize this book's main contributions and offer some concluding remarks.
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Study Case: Identifying Influential Papers In A Citation Network

Large collections of academic documents are now available through repositories such as Google Scholar and arXiv. From the data available in this kind of collections it is possible to construct diverse models to abstract different types of phenomena in the academic ecosystem. Examples of such models are citation networks. The objective of the analysis of a citation network is to discover and study the existing relationships among a set of scientific articles. Such relationships have the potential of revealing the evolution of a research field and the identification of influential articles that contributed to the development of a knowledge field. However, the more information available to build the network, the greater the difficulty in extracting useful information from such a collection of data.

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