the structure of computer science knowledge network

Lehrstuhl Informatik 5(Informationssysteme)

Prof. Dr. Matthias JarkeI5-PK-0810-1

Manh Cuong PhamRalf Klamma

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The Structure of the Computer Science Knowledge Network

Manh Cuong Pham, Ralf KlammaInformation Systems and Database Technology

RWTH Aachen, Germany

Odense, Denmark, August 09, 2010

ASONAM 2010




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Agenda

Introduction SNA as a knowledge discovery method Data sets: DBLP and CiteSeerX Network visualization Venue ranking Conclusions and Outlook




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Introduction

Digital libraries (in computer science)- DBLP, ACM DL, IEEE Explorer, CiteSeerX, etc.- Digital media for scientific knowledge conservation

- Publications- Venues

- Development of research communities & research areas

- Knowledge discovery: Citation analysis, usage-analysis, etc.

- Digital libraries in Web 2.0: Mendeley, ResearchGate etc.

Problems- Structure of computer science knowledge- Existing research fields - The interconnection between fields

VLDB community in 2006 (DBLP)

VLDB community in 1990 (DBLP)




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Motivations Scientometrics

- Unit of analysis: journals- Knowledge mapping: building, visualizing and analyzing the knowledge network- Methods:

- Citation analysis [Boyack 2005]

- Content analysis- Log-data (usage data) analysis [Bollen 2009]

- Data sets: - Journal Citation Index (JCR)- Science Citation Index (SCI)- Social Science Citation Index (SSCI), etc.

Problem– Computer science conferences




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Our Approach

Combination of large-scale digital libraries- DBLP- CiteSeer X

Citation analysis- Bibliographical coupling at venue level (conferences, journals) - Similarity measures

SNA as a knowledge discovery method- Visual analytics- Cluster analysis- SNA measures: PageRank, betweenness, hub, authority scores etc.




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Data Sets DBLP (http://www.informatik.uni-trier.de/~ley/db/)

- 788,259 author’s names- 1,226,412 publications- 3,490 venues (conferences, workshops, journals)

CiteSeerX (http://citeseerx.ist.psu.edu/)- 7,385,652 publications (including publications in reference lists)- 22,735,240 citations- Over 4 million author’s names

Combination- Canopy clustering [McCallum 2000]- Result: 864,097 matched pairs - On average: venues cite 2306 and

are cited 2037 times




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Network Creation and Pre-processing

Knowledge network- Aggregate bibliography coupling counts at venue level- Undirected graph G(V, E), where V: venues, E: edges weighted by cosine

similarity

- Threshold: - Clustering: density-based algorithm [Neuman 2004, Clauset 2004]- Network visualization: force-directed paradigm [Fruchterman 1991]

Knowledge flow network- Aggregate bibliography coupling counts at venue level- Threshold: citation counts >= 50 Domains from Microsoft Academic Search

(http://academic.research.microsoft.com/)

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Knowledge Network:the Visualization




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Knowledge Network:Clustering




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Interdisciplinary Venues:Top Betweenness Centrality




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High Prestige Series:Top PageRank




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Conclusions and Future Research SNA does help to gain an insight into the computer science knowledge Knowledge network in computer science

- Highly clustered, large clusters form the core of computer science research- Research fields are interconnected- Interdisciplinary venues

Outlook- More digital libraries should be integrated: ACM, IEEE, CEUR-WS.org, etc.- Usage analysis- Dynamic analysis of knowledge network




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Questions ?

http://bosch.informatik.rwth-aachen.de:5080/AERCS/