Discovering Research Communities by Clustering Bibliographical Data

Muhlenbach, Fabrice; Lallich, Stéphane

doi:10.1109/wi-iat.2010.117

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…The problem on scientific collaboration networks have been well-studied. Most works investigate the structure of scientific research, focusing on the author collaboration work [1,2,3,5,8,9]. However, there have not been much work on investigating conference rating.…”

Section: Related Workmentioning

confidence: 99%

Investigations on Rating Computer Sciences Conferences

Effendy

Yap

2016

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

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The rating of Computer Science (CS) conferences are important as it influences how papers published at the conferences and may also be used to evaluate research. In this paper, we proposed a method, rsIT, based on a small given set of top conference (pivots) and a relatedness measure based this set as well as basic baseline methods using citation count and field rating. We experimented with a snapshot dataset from Microsoft Academic Graph together with conference data from Microsoft Academic Search. We evaluated the conference ratings from our methods with the CCF conference rating list. We showed that rsIT correlates well with CCF rating and correlates better than ratings from using a baseline ranking with citation count or field rating.

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Section: Related Workmentioning

confidence: 99%

Investigations on Rating Computer Sciences Conferences

Effendy

Yap

2016

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

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“…Muhlenbach et al proposed to discover research communities [10]. They proposed a graph-based clustering method in the case of conferences and authors.…”

Section: Bibliographic Datamentioning

confidence: 99%

OLAP on Information Networks: A New Framework for Dealing with Bibliographic Data

Jakawat

Favre

Loudcher

2014

Advances in Intelligent Systems and Computing

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Abstract. In the context of decision making, data warehouses support OLAP technology and they have been very useful for efficient analysis onto structured data. For several years, OLAP is also used to analyze and visualize more complex data. Now, many data sets of interest can be described as a linked collection of interrelated objects. They could be represented as heterogeneous information networks, in which there are multiple object and link types. In this paper, we are focusing on bibliographic data. This type of data constitutes a rich source that is the starting point of research on bibliometrics, scientometrics domains. In this context, we discuss the interest of combining information networks, OLAP and data mining technologies. We propose a framework to materialize this combination and discuss the main challenges to build this framework. The basic idea is to be able to analyze various networks built from the bibliographic data representing different points of view (authors networks, citations networks...) and their dynamic.

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